/*-------------------------------------------------------------------- */ /* SAS(R) Survival Analysis Techniques for Medical Research, Second Edition*/ /* by Dr. Alan B. Cantor */ /* Copyright(c) 2003 by SAS Institute Inc., Cary, NC, USA */ /* SAS Publications order # 58416 */ /* ISBN 1-59047-135-0 */ /*-------------------------------------------------------------------------*/ /* */ /* This material is provided "as is" by SAS Institute Inc. and the */ /* author. There are no warranties, express or implied, as to */ /* merchantability or fitness for a particular purpose regarding the */ /* materials or code contained herein. Neither the Institute nor the */ /* author is responsible for errors in this material as it now exists */ /* or will exist, nor does the Institute or author provide technical */ /* support for it. */ /*-------------------------------------------------------------------------*/ /* Date Last Updated: 09Feb07 */ /*-------------------------------------------------------------------------*/ */ /* Questions or problem reports concerning this material may be */ /* addressed to the author: */ /* */ /* SAS Institute Inc. */ /* Books by Users */ /* Attn: Alan Cantor, Ph.D. */ /* SAS Campus Drive */ /* Cary, NC 27513 */ /* */ /* */ /* If you prefer, you can send email to: sasbbu@sas.com */ /* Use this for subject field: Comments for Alan Cantor */ /* */ /*-------------------------------------------------------------------------*/ This file contains data sets, example code, and macro code used in the book, "SAS(R) Survival Analysis Techniques for Medical Research, Second Edition" by Alan B. Cantor, Ph.D. The file lists, in order, -- data sets used in the book -- example code used in the book -- macros used in the book ************************************************************************* ************************************************************************* DATA SETS USED IN THIS BOOK *************************************************** /* The following data set is given on page 29, */ /* where it is used to produce output 2.1 and 2.2 */ *************************************************** data leuk; input time d @@; datalines; 0.0493 1 0.2849 1 0.4082 1 0.8767 1 0.8877 1 1.1233 1 1.2247 0 1.3753 1 1.5425 1 1.5836 1 1.7397 1 1.7589 1 1.7726 1 1.9233 1 1.9562 0 2.0493 1 2.2986 1 2.3425 1 3.7315 1 4.0548 1 4.0685 0 4.5863 1 4.9534 1 5.1534 0 5.7315 0 5.8493 1 5.8685 1 6.0712 0 6.1151 0 7.3781 0 7.6630 0 8.0438 0 8.1890 0 8.2055 0 8.2548 0 8.4274 0 8.4521 0 8.7589 0 9.0356 0 9.8959 0 9.9151 0 9.9178 0 10.1151 0 10.4027 0 10.6000 0 10.6603 1 10.6685 0 10 . 7260 0 10.9260 0 10.9370 0 11.2027 0 11.4548 0 11.4712 0 11.5589 0 11.6082 0 11.6164 0 11.6521 0 11.7123 0 11.7671 0 11.8466 0 11.8575 0 11.8685 0 11.9863 0 12.0082 0 ; *************************************************** /* The following data set is described on page 45,*/ /* where it is used to produce output 2.8 */ *************************************************** data; input l r; datalines 0.0000 0.0493 0.0000 0.2849 0.0000 0.4082 0.0517 0.8767 0.0627 0.8877 0.2983 1.1233 1.2247 15.0000 0.5503 1.3753 0.7175 1.5425 0.7586 1.5836 0.9147 1.7397 0.9339 1.7589 0.9476 1.7726 1.0983 1.9233 1.9562 15.0000 1.2243 2.0493 1.4736 2.2986 1.5175 2.3425 2.9065 3.7315 3.2298 4.0548 4.0685 15.0000 3.7613 4.5863 4.1284 4.9534 5.1534 15.0000 5.7315 15.0000 5.0243 5.8493 5.0435 5.8685 6.0712 15.0000 6.1151 15.0000 7.3781 15.0000 7.6630 15.0000 8.0438 15.0000 8.1890 15.0000 8.2548 15.0000 8.4274 15.0000 8.4521 15.0000 8.7589 15.0000 9.0356 15.0000 9.8959 15.0000 9.9151 15.0000 9.9178 15.0000 10.1151 15.0000 10.4027 15.0000 10.6000 15.0000 9.8353 10.6603 10.6685 15.0000 10.7260 15.0000 10.9260 15.0000 10.9370 15.0000 11.2027 15.0000 11.4548 15.0000 11.4712 15.0000 11.5589 15.0000 11.6082 15.0000 11.6164 15.0000 11.6521 15.0000 11.7123 15.0000 11.7671 15.0000 11.8466 15.0000 11.8575 15.0000 11.8685 15.0000 11.9863 15.0000 12.0082 15.0000 ; *************************************************** /* The following data set is mentioned on page 60,*/ /* where it is used to produce output 3.1-3.4. It */ /* is also needed for exercise 3.6.1 */ *************************************************** data breast; input @1 stage $3. @9 cens @17 years @33 agegrp $5.; datalines; 0 0.0465434634 80+ 0 8.3093771389 0-59 0 7.0499657769 0-59 0 2.2614647502 0-59 0 0.3449691992 0-59 0 0.4216290212 0-59 0 0.0273785079 60-69 0 2.2094455852 60-69 0 3.5838466804 70-79 0 0 70-79 0 0.0164271047 70-79 0 0.1533196441 70-79 0 4.3066392882 70-79 0 2.3627652293 70-79 1 1.4976043806 80+ 1 0.4517453799 80+ 0 0 8.1368925394 0-59 0 0 2.9185489391 0-59 0 0 5.7522245038 0-59 0 0 1.054072553 80+ 0 0 1.9438740589 0-59 0 0 5.5359342916 0-59 0 0 5.0102669405 0-59 0 0 4.4982888433 0-59 0 0 2.4449007529 0-59 0 0 1.7850787132 0-59 0 0 6.7241615332 60-69 0 0 1.3305954825 60-69 0 0 0.1368925394 60-69 0 0 7.4332648871 70-79 0 0 2.1409993155 70-79 0 0 1.5660506502 80+ 0 0 9.1498973306 0-59 0 0 6.9678302533 0-59 0 0 7.0554414784 0-59 0 0 6.6694045175 0-59 0 0 7.279945243 0-59 0 0 6.9240246407 0-59 0 1 5.5906913073 0-59 0 0 7.0746064339 0-59 0 0 7.1813826146 0-59 0 0 6.8473648186 0-59 0 0 6.6830937714 0-59 0 0 6.1409993155 0-59 0 0 5.7796030116 0-59 0 0 5.5441478439 0-59 0 0 5.0239561944 0-59 0 0 5.1745379877 0-59 0 0 5.6673511294 0-59 0 0 5.5003422313 0-59 0 0 5.5605749487 0-59 0 0 5.8836413415 0-59 0 0 5.3963039014 0-59 0 0 5.2156057495 0-59 0 0 4.9500342231 0-59 0 0 5.0349075975 0-59 0 0 5.492128679 0-59 0 0 4.900752909 0-59 0 0 4.9336071184 0-59 0 0 4.9363449692 0-59 0 0 2.2395619439 0-59 0 0 4.3668720055 0-59 0 0 4.4298425736 0-59 0 0 3.3785078713 0-59 0 0 3.8877481177 0-59 0 0 3.832991102 0-59 0 0 4.0109514031 0-59 0 0 4.4188911704 0-59 0 0 3.0280629706 0-59 0 0 4.3997262149 0-59 0 0 2.6611909651 0-59 0 0 3.9425051335 0-59 0 0 3.5509924709 0-59 0 0 4.0876112252 0-59 0 0 3.1403148528 0-59 0 0 3.0691307324 0-59 0 0 3.1019849418 0-59 0 0 2.8911704312 0-59 0 0 2.1629021218 0-59 0 0 2.3134839151 0-59 0 0 2.6967830253 0-59 0 0 2.2450376454 0-59 0 0 2.2450376454 0-59 0 0 1.5797399042 0-59 0 0 1.8945927447 0-59 0 0 1.8754277892 0-59 0 0 1.9247091034 0-59 0 0 1.3990417522 0-59 0 0 1.87816564 0-59 0 0 0.9582477755 0-59 0 0 0.8898015058 0-59 0 0 1.9055441478 0-59 0 0 1.3278576318 0-59 0 0 1.1143052704 0-59 0 0 1.3470225873 0-59 0 0 1.1718001369 0-59 0 0 0.9938398357 0-59 0 0 0.2874743326 0-59 0 0 0.1724845996 0-59 0 0 0.5201916496 0-59 0 0 0.4188911704 0-59 0 0 0.2409308693 0-59 0 0 0.4161533196 0-59 0 0 0.3805612594 0-59 0 0 0.3668720055 0-59 0 0 0.1861738535 0-59 0 0 0.205338809 0-59 0 0 0.3778234086 0-59 0 0 8.5174537988 0-59 0 0 3.5838466804 0-59 0 0 0.4298425736 0-59 0 0 6.3244353183 0-59 0 0 4.0848733744 0-59 0 0 4.5503080082 0-59 0 0 8.553045859 60-69 0 0 8.3258042437 60-69 0 0 7.5181382615 60-69 0 0 6.5133470226 60-69 0 0 6.9185489391 60-69 0 0 6.2176591376 60-69 0 0 6.2833675565 60-69 0 0 6.4120465435 60-69 0 0 5.9767282683 60-69 0 0 5.0349075975 60-69 0 0 5.0595482546 60-69 0 0 5.3004791239 60-69 0 0 5.1170431211 60-69 0 0 5.0513347023 60-69 0 0 4.9637234771 60-69 0 0 4.3915126626 60-69 0 0 3.9370294319 60-69 0 0 4.5804243669 60-69 0 0 3.6741957563 60-69 0 0 3.4798083504 60-69 0 0 4.5065023956 60-69 0 0 2.9295003422 60-69 0 0 3.0006844627 60-69 0 1 1.3360711841 60-69 0 0 3.5290896646 60-69 0 0 2.9158110883 60-69 0 0 2.7953456537 60-69 0 0 1.9739904175 60-69 0 0 2.3299110198 60-69 0 0 2.0150581793 60-69 0 0 2.1163586585 60-69 0 0 1.7850787132 60-69 0 0 1.582477755 60-69 0 0 1.6454483231 60-69 0 0 1.2292950034 60-69 0 0 1.0568104038 60-69 0 0 1.5578370979 60-69 0 0 1.1416837782 60-69 0 0 1.1498973306 60-69 0 0 1.106091718 60-69 0 0 0.93908282 60-69 0 0 1.1033538672 60-69 0 0 0.0793976728 60-69 0 0 0.3394934976 60-69 0 0 0.3093771389 60-69 0 0 0.1368925394 60-69 0 0 0.4134154689 60-69 0 0 0.295687885 60-69 0 0 0.4024640657 60-69 0 0 7.1676933607 60-69 0 0 6.2997946612 60-69 0 0 3.485284052 60-69 0 1 2.2149212868 60-69 0 0 1.7221081451 60-69 0 0 8.4955509925 70-79 0 0 8.0821355236 70-79 0 1 5.6016427105 70-79 0 0 6.855578371 70-79 0 0 7.4004106776 70-79 0 0 6.507871321 70-79 0 0 6.3299110198 70-79 0 0 5.9520876112 70-79 0 0 5.4428473648 70-79 0 0 6.2286105407 70-79 0 0 5.1279945243 70-79 0 0 4.295687885 70-79 0 0 4.0273785079 70-79 0 0 4.3449691992 70-79 0 0 4.6105407255 70-79 0 0 3.2553045859 70-79 0 0 3.1567419576 70-79 0 0 3.2470910335 70-79 0 0 2.6776180698 70-79 0 0 2.7049965777 70-79 0 0 2.546201232 70-79 0 0 2.1793292266 70-79 0 0 1.8754277892 70-79 0 0 1.5167693361 70-79 0 0 1.1663244353 70-79 0 0 1.0266940452 70-79 0 0 0.9199178645 70-79 0 0 1.1663244353 70-79 0 0 0.2108145106 70-79 0 0 6.9404517454 80+ 0 0 5.3771389459 80+ 0 0 4.9281314168 80+ 0 0 2.160164271 80+ 0 0 0.476386037 80+ 0 0 0.2080766598 80+ I 0 5.8370978782 0-59 I 0 1.1909650924 0-59 I 0 0.1861738535 70-79 I 0 4.3258042437 80+ I 0 6.173853525 0-59 I 0 0.3997262149 0-59 I 0 6.1902806297 70-79 I 0 4.4052019165 0-59 I 0 1.2375085558 0-59 I 1 4.3039014374 0-59 I 0 5.8288843258 0-59 I 1 3.5263518138 0-59 I 0 0.9637234771 0-59 I 0 6.2094455852 60-69 I 0 3.909650924 60-69 I 0 7.7399041752 60-69 I 1 2.4832306639 60-69 I 0 1.4483230664 60-69 I 0 0.2327173169 70-79 I 0 5.0951403149 70-79 I 0 0.1122518823 70-79 I 0 8.1451060917 0-59 I 0 7.9452429843 0-59 I 1 4.3696098563 0-59 I 0 1.9958932238 0-59 I 1 1.9329226557 0-59 I 0 8.0821355236 0-59 I 0 6.6639288159 0-59 I 0 6.9815195072 0-59 I 0 6.6858316222 0-59 I 0 6.5434633812 0-59 I 0 1.4592744695 0-59 I 0 6.7214236824 0-59 I 0 5.4592744695 0-59 I 0 5.8480492813 0-59 I 0 5.620807666 0-59 I 0 4.6105407255 0-59 I 0 4.7255304586 0-59 I 0 4.4106776181 0-59 I 0 4.6023271732 0-59 I 0 4.4709103354 0-59 I 0 3.9151266256 0-59 I 1 3.7234770705 0-59 I 0 4.4079397673 0-59 I 0 4.2765229295 0-59 I 0 3.6331279945 0-59 I 0 3.2470910335 0-59 I 0 2.6228610541 0-59 I 0 3.0965092402 0-59 I 0 2.9404517454 0-59 I 0 3.0773442847 0-59 I 0 2.9130732375 0-59 I 0 2.3983572895 0-59 I 0 2.0314852841 0-59 I 0 1.7275838467 0-59 I 0 2.0287474333 0-59 I 0 2.0095824778 0-59 I 0 2.0862422998 0-59 I 0 2.5544147844 0-59 I 0 1.1197809719 0-59 I 0 1.2156057495 0-59 I 0 1.2073921971 0-59 I 0 0.7775496235 0-59 I 0 1.4592744695 0-59 I 0 0.9144421629 0-59 I 0 0.3230663929 0-59 I 0 1.0130047912 0-59 I 0 0.2327173169 0-59 I 0 0.5119780972 0-59 I 0 0.386036961 0-59 I 0 0.3449691992 0-59 I 0 0.2929500342 0-59 I 0 0.3832991102 0-59 I 0 0.3230663929 0-59 I 0 0.2874743326 0-59 I 0 0.4572210815 0-59 I 0 0.3258042437 0-59 I 0 0.2929500342 0-59 I 0 0.4134154689 0-59 I 0 7.4277891855 0-59 I 0 6.6721423682 0-59 I 0 7.8932238193 0-59 I 0 6.9240246407 0-59 I 0 6.984257358 0-59 I 0 6.1683778234 0-59 I 0 6.3901437372 0-59 I 0 6.9349760438 0-59 I 0 5.7056810404 0-59 I 0 5.7467488022 0-59 I 0 5.3442847365 0-59 I 0 5.568788501 0-59 I 0 5.2511978097 0-59 I 0 5.3415468857 0-59 I 0 3.3839835729 0-59 I 0 4.1834360027 0-59 I 0 3.356605065 0-59 I 0 3.4551676934 0-59 I 0 3.0663928816 0-59 I 0 2.9212867899 0-59 I 0 2.71321013 0-59 I 0 2.4859685147 0-59 I 0 2.5407255305 0-59 I 0 2.855578371 0-59 I 0 3.0088980151 0-59 I 0 1.7823408624 0-59 I 0 1.5934291581 0-59 I 0 0.1396303901 0-59 I 0 1.5359342916 0-59 I 0 1.1115674196 0-59 I 0 1.9438740589 0-59 I 0 0.8624229979 0-59 I 0 1.4976043806 0-59 I 0 1.0513347023 0-59 I 0 1.054072553 0-59 I 0 0.4955509925 0-59 I 0 0.2327173169 0-59 I 0 0.1697467488 0-59 I 0 0.386036961 0-59 I 0 0.2874743326 0-59 I 0 0.372347707 0-59 I 0 0.3696098563 0-59 I 0 0.3011635866 0-59 I 0 0.3641341547 0-59 I 0 0.4544832307 0-59 I 0 8.2628336756 0-59 I 0 8.5119780972 0-59 I 1 5.1197809719 0-59 I 0 7.819301848 0-59 I 0 7.8740588638 0-59 I 0 7.0554414784 0-59 I 0 6.9678302533 0-59 I 0 8.0438056126 0-59 I 0 7.4195756331 0-59 I 0 7.5865845311 0-59 I 0 7.0527036277 0-59 I 0 6.7816563997 0-59 I 0 6.4257357974 0-59 I 0 5.5770020534 0-59 I 0 6.135523614 0-59 I 0 5.6700889802 0-59 I 0 5.4483230664 0-59 I 0 5.054072553 0-59 I 0 5.4072553046 0-59 I 0 4.8323066393 0-59 I 1 2.5516769336 0-59 I 0 4.8624229979 0-59 I 0 5.1115674196 0-59 I 0 4.5968514716 0-59 I 0 2.8199863107 0-59 I 0 5.0184804928 0-59 I 0 4.0136892539 0-59 I 0 4.810403833 0-59 I 0 4.0438056126 0-59 I 0 4.1368925394 0-59 I 0 4.2409308693 0-59 I 0 4.0492813142 0-59 I 0 3.1841204654 0-59 I 0 3.1868583162 0-59 I 0 3.0472279261 0-59 I 1 1.4264202601 0-59 I 0 2.4339493498 0-59 I 0 3.0444900753 0-59 I 0 2.7515400411 0-59 I 0 2.4476386037 0-59 I 0 2.3901437372 0-59 I 0 2.2450376454 0-59 I 0 2.6201232033 0-59 I 0 2.71321013 0-59 I 0 2.3134839151 0-59 I 0 2.1136208077 0-59 I 0 2.507871321 0-59 I 0 2.0396988364 0-59 I 0 1.8425735797 0-59 I 0 2.2039698836 0-59 I 0 2.2970568104 0-59 I 0 1.787816564 0-59 I 0 1.8015058179 0-59 I 0 2.0670773443 0-59 I 0 1.8945927447 0-59 I 0 2.1683778234 0-59 I 0 1.18275154 0-59 I 0 1.9520876112 0-59 I 0 1.9520876112 0-59 I 0 1.5770020534 0-59 I 0 1.1663244353 0-59 I 0 1.1362080767 0-59 I 0 1.1088295688 0-59 I 0 1.1307323751 0-59 I 0 1.3497604381 0-59 I 0 1.4428473648 0-59 I 0 1.0212183436 0-59 I 0 1.0102669405 0-59 I 0 0.3312799452 0-59 I 0 0.8678986995 0-59 I 0 0.2984257358 0-59 I 0 0.7501711157 0-59 I 0 0.4955509925 0-59 I 0 0.3011635866 0-59 I 0 0.3641341547 0-59 I 0 0.2792607803 0-59 I 0 0.295687885 0-59 I 0 0.7419575633 0-59 I 0 0.2436687201 0-59 I 0 0.3942505133 0-59 I 0 0.3230663929 0-59 I 0 0.3449691992 0-59 I 0 0.2929500342 0-59 I 0 0.3258042437 0-59 I 0 7.961670089 0-59 I 0 6.9404517454 0-59 I 0 6.4394250513 0-59 I 0 6.6064339493 0-59 I 0 0.8952772074 0-59 I 0 4.205338809 0-59 I 0 6.0561259411 0-59 I 0 5.5277207392 0-59 I 0 6.4394250513 0-59 I 0 5.9000684463 0-59 I 0 5.5961670089 0-59 I 0 5.9603011636 0-59 I 0 5.8507871321 0-59 I 0 5.7549623546 0-59 I 0 5.6919917864 0-59 I 0 5.9575633128 0-59 I 0 5.28678987 0-59 I 0 5.7741273101 0-59 I 1 4.7008898015 0-59 I 0 4.8706365503 0-59 I 0 5.3169062286 0-59 I 1 3.0472279261 0-59 I 0 5.0047912389 0-59 I 1 4.9993155373 0-59 I 0 5.1115674196 0-59 I 0 5.0814510609 0-59 I 0 4.7885010267 0-59 I 0 4.6324435318 0-59 I 0 4.2792607803 0-59 I 0 4.6488706366 0-59 I 0 4.6214921287 0-59 I 0 4.3832991102 0-59 I 1 4.6735112936 0-59 I 0 4.1368925394 0-59 I 0 4.7173169062 0-59 I 0 4.6954140999 0-59 I 0 4.1889117043 0-59 I 0 4.2272416153 0-59 I 0 4.34770705 0-59 I 0 4.0164271047 0-59 I 0 4.2108145106 0-59 I 0 4.3997262149 0-59 I 0 2.9869952088 0-59 I 0 3.3155373032 0-59 I 0 3.5920602327 0-59 I 0 3.4743326489 0-59 I 0 2.855578371 0-59 I 0 2.9295003422 0-59 I 0 2.1245722108 0-59 I 0 2.1190965092 0-59 I 0 2.1054072553 0-59 I 0 0.2299794661 0-59 I 0 2.135523614 0-59 I 0 2.1793292266 0-59 I 0 2.0342231348 0-59 I 0 2.3244353183 0-59 I 0 1.9904175222 0-59 I 0 2.3134839151 0-59 I 0 2.2149212868 0-59 I 1 1.3004791239 0-59 I 0 2.1136208077 0-59 I 0 1.6783025325 0-59 I 0 1.1252566735 0-59 I 0 1.5578370979 0-59 I 0 1.0184804928 0-59 I 0 0.6160164271 0-59 I 0 1.1033538672 0-59 I 0 0.8898015058 0-59 I 0 1.3004791239 0-59 I 0 0.3285420945 0-59 I 0 1.0869267625 0-59 I 0 1.1170431211 0-59 I 0 0.34770705 0-59 I 0 1.3880903491 0-59 I 0 0.2327173169 0-59 I 0 0.3641341547 0-59 I 0 0.4024640657 0-59 I 0 0.34770705 0-59 I 0 0.3148528405 0-59 I 0 0.3832991102 0-59 I 0 6.3819301848 0-59 I 0 8.7748117728 60-69 I 0 8.2902121834 60-69 I 0 7.8767967146 60-69 I 1 4.5256673511 60-69 I 0 8.3367556468 60-69 I 0 8.219028063 60-69 I 0 6.546201232 60-69 I 0 6.1683778234 60-69 I 0 6.1409993155 60-69 I 0 6.1437371663 60-69 I 0 6.2778918549 60-69 I 0 5.4757015743 60-69 I 0 5.5058179329 60-69 I 0 4.9911019849 60-69 I 0 4.7501711157 60-69 I 0 4.6625598905 60-69 I 0 5.5496235455 60-69 I 0 5.2292950034 60-69 I 0 3.7727583847 60-69 I 0 4.219028063 60-69 I 0 4.5119780972 60-69 I 0 2.9404517454 60-69 I 0 3.2005475702 60-69 I 0 3.4825462012 60-69 I 0 3.1211498973 60-69 I 0 3.2553045859 60-69 I 0 3.1403148528 60-69 I 0 2.8008213552 60-69 I 0 2.9486652977 60-69 I 0 3.1622176591 60-69 I 0 3.2005475702 60-69 I 0 2.9924709103 60-69 I 0 2.5626283368 60-69 I 0 2.9130732375 60-69 I 0 2.4120465435 60-69 I 0 1.8863791923 60-69 I 0 1.4784394251 60-69 I 0 1.1718001369 60-69 I 0 1.4318959617 60-69 I 0 1.5003422313 60-69 I 0 1.0650239562 60-69 I 0 0.8651608487 60-69 I 0 1.1991786448 60-69 I 0 0.7830253251 60-69 I 0 0.2108145106 60-69 I 0 0.3696098563 60-69 I 0 0.3449691992 60-69 I 0 0.2655715264 60-69 I 0 0.5366187543 60-69 I 0 0.4681724846 60-69 I 0 7.8083504449 60-69 I 0 7.7891854894 60-69 I 0 6.8090349076 60-69 I 0 0.9719370294 60-69 I 0 6.8227241615 60-69 I 0 5.0787132101 60-69 I 0 5.106091718 60-69 I 0 5.158110883 60-69 I 0 4.3832991102 60-69 I 0 4.2765229295 60-69 I 0 4.038329911 60-69 I 0 3.9206023272 60-69 I 0 4.0438056126 60-69 I 0 3.7453798768 60-69 I 0 3.3976728268 60-69 I 0 2.6803559206 60-69 I 0 3.1786447639 60-69 I 0 2.6475017112 60-69 I 0 2.0561259411 60-69 I 0 2.045174538 60-69 I 0 2.1464750171 60-69 I 0 1.2813141684 60-69 I 0 1.401779603 60-69 I 0 1.6837782341 60-69 I 0 1.1909650924 60-69 I 0 0.8049281314 60-69 I 0 0.6187542779 60-69 I 0 0.2245037645 60-69 I 0 0.0629705681 60-69 I 0 0.3121149897 60-69 I 0 0.3011635866 60-69 I 1 6.8473648186 60-69 I 0 7.6167008898 60-69 I 0 7.6796714579 60-69 I 0 7.5920602327 60-69 I 0 7.5290896646 60-69 I 0 7.4934976044 60-69 I 0 7.5208761123 60-69 I 0 7.0198494182 60-69 I 0 7.0034223135 60-69 I 0 6.6338124572 60-69 I 0 6.6091718001 60-69 I 0 6.7597535934 60-69 I 0 6.8583162218 60-69 I 0 6.71321013 60-69 I 0 5.8836413415 60-69 I 0 6.4229979466 60-69 I 0 6.2532511978 60-69 I 0 5.8042436687 60-69 I 0 5.6481861739 60-69 I 0 5.0102669405 60-69 I 0 6.0232717317 60-69 I 0 4.9418206708 60-69 I 1 3.9370294319 60-69 I 0 5.158110883 60-69 I 0 4.3915126626 60-69 I 0 4.1971252567 60-69 I 0 3.9206023272 60-69 I 1 2.8966461328 60-69 I 0 3.4880219028 60-69 I 0 3.5290896646 60-69 I 0 3.5674195756 60-69 I 0 3.5975359343 60-69 I 0 3.0691307324 60-69 I 0 3.0061601643 60-69 I 0 3.1238877481 60-69 I 0 2.5516769336 60-69 I 0 1.8288843258 60-69 I 0 2.1437371663 60-69 I 0 2.0150581793 60-69 I 0 1.4893908282 60-69 I 0 1.234770705 60-69 I 0 0.810403833 60-69 I 0 1.1143052704 60-69 I 0 1.0841889117 60-69 I 0 1.0869267625 60-69 I 0 1.1663244353 60-69 I 0 0.3613963039 60-69 I 0 0.2737850787 60-69 I 0 0.3504449008 60-69 I 0 0.3011635866 60-69 I 0 0.2026009582 60-69 I 0 0.3586584531 60-69 I 0 0.4188911704 60-69 I 0 0.3887748118 60-69 I 0 0.3449691992 60-69 I 0 0.3230663929 60-69 I 0 7.2689938398 60-69 I 0 7.6057494867 60-69 I 1 4.9117043121 60-69 I 1 7.1978097194 60-69 I 0 6.8829568789 60-69 I 0 6.431211499 60-69 I 0 6.4229979466 60-69 I 0 6.5215605749 60-69 I 0 6.3983572895 60-69 I 0 5.2429842574 60-69 I 0 5.0431211499 60-69 I 0 5.2511978097 60-69 I 0 5.0924024641 60-69 I 0 4.6926762491 60-69 I 0 3.7152635181 60-69 I 0 3.8740588638 60-69 I 0 3.2553045859 60-69 I 0 3.1019849418 60-69 I 0 3.2197125257 60-69 I 0 2.1054072553 60-69 I 0 2.1108829569 60-69 I 0 1.8590006845 60-69 I 0 2.0999315537 60-69 I 0 2.2587268994 60-69 I 0 2.2450376454 60-69 I 0 2.0752908966 60-69 I 0 0.2847364819 60-69 I 0 1.0924024641 60-69 I 0 1.3497604381 60-69 I 0 1.1006160164 60-69 I 0 0.8076659822 60-69 I 0 0.8323066393 60-69 I 0 0.3066392882 60-69 I 0 0.3066392882 60-69 I 0 0.2737850787 60-69 I 0 0.3778234086 60-69 I 0 0.3312799452 60-69 I 0 8.1368925394 70-79 I 0 8.2737850787 70-79 I 0 8.3504449008 70-79 I 0 5.6700889802 70-79 I 0 5.0431211499 70-79 I 0 5.3059548255 70-79 I 0 5.1690622861 70-79 I 0 5.0978781656 70-79 I 0 3.4496919918 70-79 I 0 3.9753593429 70-79 I 0 4.4845995893 70-79 I 0 4.4490075291 70-79 I 0 3.4770704997 70-79 I 0 3.4880219028 70-79 I 0 2.4038329911 70-79 I 0 1.9685147159 70-79 I 0 2.392881588 70-79 I 0 2.2833675565 70-79 I 0 1.7084188912 70-79 I 0 2.1409993155 70-79 I 0 1.1362080767 70-79 I 0 1.1279945243 70-79 I 0 0.9911019849 70-79 I 0 1.1882272416 70-79 I 0 1.2484599589 70-79 I 0 0.0602327173 70-79 I 0 0.219028063 70-79 I 0 0.334017796 70-79 I 0 0.34770705 70-79 I 0 0.3367556468 70-79 I 0 0.2409308693 70-79 I 1 5.3196440794 70-79 I 0 5.9055441478 70-79 I 0 5.9383983573 70-79 I 0 4.9226557153 70-79 I 0 5.2977412731 70-79 I 0 3.1759069131 70-79 I 0 2.5845311431 70-79 I 0 2.0123203285 70-79 I 0 1.3388090349 70-79 I 0 1.1279945243 70-79 I 0 1.4209445585 70-79 I 0 0.2847364819 70-79 I 0 0 70-79 I 0 7.7125256674 70-79 I 0 6.5270362765 70-79 I 0 6.8637919233 70-79 I 0 7.0308008214 70-79 I 0 0.2546201232 70-79 I 0 6.0479123888 70-79 I 0 6.1902806297 70-79 I 0 6.1930184805 70-79 I 0 5.7604380561 70-79 I 0 5.4948665298 70-79 I 1 2.0643394935 70-79 I 0 4.9719370294 70-79 I 0 3.6824093087 70-79 I 0 3.022587269 70-79 I 0 3.2963723477 70-79 I 0 2.7761806982 70-79 I 1 2.2149212868 70-79 I 0 2.8610540726 70-79 I 0 3.1293634497 70-79 I 0 2.0479123888 70-79 I 0 1.9575633128 70-79 I 0 1.7932922656 70-79 I 0 1.2840520192 70-79 I 0 1.2703627652 70-79 I 0 0.8323066393 70-79 I 0 0.9527720739 70-79 I 0 1.1718001369 70-79 I 0 0.334017796 70-79 I 0 0.3258042437 70-79 I 0 0.372347707 70-79 I 0 0.2929500342 70-79 I 0 0.3449691992 70-79 I 0 7.9671457906 70-79 I 1 6.2970568104 70-79 I 0 5.4866529774 70-79 I 0 5.3798767967 70-79 I 0 5.311430527 70-79 I 0 4.5065023956 70-79 I 1 2.3299110198 70-79 I 0 4.1642710472 70-79 I 0 4.514715948 70-79 I 0 3.8165639973 70-79 I 0 3.3976728268 70-79 I 0 4.0438056126 70-79 I 0 1.1115674196 70-79 I 0 0.0793976728 70-79 I 0 0.3312799452 70-79 I 1 4.4188911704 80+ I 1 1.4154688569 80+ I 1 3.2306639288 80+ I 0 2.0342231348 80+ I 0 1.106091718 80+ I 0 0.900752909 80+ I 0 0.3258042437 80+ I 0 0.3969883641 80+ I 1 3.6468172485 80+ I 0 5.6180698152 80+ I 0 3.4880219028 80+ I 0 3.5290896646 80+ I 0 2.0150581793 80+ I 0 1.1663244353 80+ I 0 0.34770705 80+ I 0 0.1916495551 80+ I 1 0.9555099247 80+ I 1 6.71321013 80+ I 1 3.5044490075 80+ I 1 0.7145790554 80+ I 0 3.318275154 80+ I 0 2.2395619439 80+ I 0 0.8377823409 80+ I 0 0.1533196441 80+ I 0 0.2464065708 80+ I 1 1.2813141684 80+ I 1 1.3963039014 80+ I 0 1.1663244353 80+ I 0 1.1362080767 80+ II 0 6.5215605749 0-59 II 0 5.6043805613 0-59 II 0 2.3189596167 0-59 II 1 4.7474332649 0-59 II 0 5.3716632444 0-59 II 1 1.5852156057 0-59 II 0 2.6091718001 0-59 II 0 5.8726899384 0-59 II 1 1.5742642026 0-59 II 1 3.2525667351 0-59 II 0 3.4305270363 0-59 II 0 0.7282683094 0-59 II 0 3.2197125257 0-59 II 1 1.9986310746 0-59 II 0 2.5325119781 0-59 II 0 0.3997262149 0-59 II 0 7.2635181383 60-69 II 1 1.0513347023 60-69 II 0 2.8008213552 60-69 II 0 3.4058863792 60-69 II 0 1.3552361396 60-69 II 1 1.1991786448 70-79 II 0 0.3696098563 70-79 II 0 1.4154688569 70-79 II 0 1.0759753593 0-59 II 0 0.1122518823 0-59 II 0 4.3915126626 60-69 II 0 8.4353182752 0-59 II 0 0.3148528405 0-59 II 0 6.160164271 0-59 II 0 4.2327173169 0-59 II 0 1.9055441478 0-59 II 0 0.424366872 0-59 II 0 3.2361396304 0-59 II 1 1.5222450376 0-59 II 0 0.7419575633 0-59 II 0 0.424366872 60-69 II 0 5.311430527 60-69 II 0 5.7467488022 60-69 II 0 0.34770705 70-79 II 0 8.6269678303 0-59 II 1 5.3497604381 0-59 II 1 1.8809034908 0-59 II 0 7.8384668036 0-59 II 0 8.5941136208 0-59 II 1 3.1978097194 0-59 II 1 5.2785763176 0-59 II 1 3.2060232717 0-59 II 1 2.3271731691 0-59 II 0 7.6112251882 0-59 II 0 7.0335386721 0-59 II 1 1.3333333333 0-59 II 1 6.4914442163 0-59 II 1 1.4236824093 0-59 II 0 6.7241615332 0-59 II 1 2.2450376454 0-59 II 0 6.0780287474 0-59 II 0 5.5003422313 0-59 II 1 1.5797399042 0-59 II 1 2.3956194387 0-59 II 0 4.8213552361 0-59 II 0 5.0595482546 0-59 II 1 4.501026694 0-59 II 0 5.015742642 0-59 II 0 3.5044490075 0-59 II 0 4.5366187543 0-59 II 0 3.8083504449 0-59 II 0 3.832991102 0-59 II 0 3.6769336071 0-59 II 0 3.1622176591 0-59 II 0 2.2340862423 0-59 II 0 2.1656399726 0-59 II 0 2.2915811088 0-59 II 1 2.5516769336 0-59 II 0 2.302532512 0-59 II 0 2.2806297057 0-59 II 0 2.2039698836 0-59 II 0 1.8179329227 0-59 II 0 1.6153319644 0-59 II 1 1.8343600274 0-59 II 0 1.3442847365 0-59 II 0 0.0711841205 0-59 II 0 1.2840520192 0-59 II 0 0.977412731 0-59 II 0 1.1006160164 0-59 II 0 0.2765229295 0-59 II 0 0.7638603696 0-59 II 0 0.8186173854 0-59 II 0 0.295687885 0-59 II 0 0.3531827515 0-59 II 0 0.3504449008 0-59 II 0 0.1642710472 0-59 II 0 6.4093086927 0-59 II 0 5.1143052704 0-59 II 1 0.514715948 0-59 II 0 4.4271047228 0-59 II 0 5.0403832991 0-59 II 0 5.0595482546 0-59 II 0 4.0793976728 0-59 II 0 3.8548939083 0-59 II 0 4.2765229295 0-59 II 0 3.8357289528 0-59 II 0 3.5509924709 0-59 II 0 3.2525667351 0-59 II 0 4.0219028063 0-59 II 0 2.8528405202 0-59 II 0 3.2772073922 0-59 II 0 2.5407255305 0-59 II 0 2.5927446954 0-59 II 0 2.2724161533 0-59 II 0 2.6502395619 0-59 II 0 1.2457221081 0-59 II 1 1.9192334018 0-59 II 0 1.4209445585 0-59 II 0 0.4736481862 0-59 II 0 0.4791238877 0-59 II 1 2.5571526352 0-59 II 1 6.945927447 0-59 II 0 7.0882956879 0-59 II 0 7.4195756331 0-59 II 0 6.5927446954 0-59 II 0 6.1984941821 0-59 II 0 6.0123203285 0-59 II 0 6.0643394935 0-59 II 0 6.3846680356 0-59 II 0 5.8316221766 0-59 II 0 5.3853524983 0-59 II 0 5.7412731006 0-59 II 0 5.0595482546 0-59 II 0 5.311430527 0-59 II 0 4.6160164271 0-59 II 0 4.8843258042 0-59 II 0 4.3696098563 0-59 II 1 2.5763175907 0-59 II 0 3.8877481177 0-59 II 0 3.3045859001 0-59 II 1 3.8877481177 0-59 II 0 4.1122518823 0-59 II 0 2.8829568789 0-59 II 0 4.0164271047 0-59 II 1 3.5044490075 0-59 II 0 4.128678987 0-59 II 0 1.18275154 0-59 II 0 3.2197125257 0-59 II 0 3.2525667351 0-59 II 0 3.4305270363 0-59 II 0 3.1293634497 0-59 II 0 3.1403148528 0-59 II 0 2.8747433265 0-59 II 0 2.6803559206 0-59 II 0 3.1704312115 0-59 II 1 1.4893908282 0-59 II 0 2.3983572895 0-59 II 1 1.3853524983 0-59 II 0 2.4065708419 0-59 II 0 2.2915811088 0-59 II 0 3.947980835 0-59 II 0 2.8884325804 0-59 II 0 1.9603011636 0-59 II 0 2.1382614648 0-59 II 0 2.2587268994 0-59 II 0 1.5277207392 0-59 II 0 2.4859685147 0-59 II 0 1.8069815195 0-59 II 0 1.2676249144 0-59 II 0 1.4373716632 0-59 II 0 1.1416837782 0-59 II 0 1.1416837782 0-59 II 0 0.6516084873 0-59 II 0 1.3333333333 0-59 II 0 0.3613963039 0-59 II 0 1.28678987 0-59 II 0 1.0924024641 0-59 II 0 1.2840520192 0-59 II 0 1.2813141684 0-59 II 0 0.3559206023 0-59 II 0 1.1115674196 0-59 II 0 0.424366872 0-59 II 0 1.144421629 0-59 II 0 0.2765229295 0-59 II 0 0.3805612594 0-59 II 0 0.3668720055 0-59 II 0 0.3121149897 0-59 II 0 0.3613963039 0-59 II 0 0.4435318275 0-59 II 0 0.4435318275 0-59 II 0 0.34770705 0-59 II 0 0.4791238877 0-59 II 0 0.2628336756 0-59 II 0 0.4490075291 0-59 II 0 0.2819986311 0-59 II 0 7.1704312115 0-59 II 1 1.2238193018 0-59 II 1 5.5468856947 0-59 II 0 7.7180013689 0-59 II 1 3.4058863792 0-59 II 1 0.93908282 0-59 II 0 7.1786447639 0-59 II 0 5.1197809719 0-59 II 0 6.9267624914 0-59 II 1 3.6577686516 0-59 II 0 6.9815195072 0-59 II 1 2.4640657084 0-59 II 1 1.735797399 0-59 II 0 4.7529089665 0-59 II 0 6.2395619439 0-59 II 0 6.5626283368 0-59 II 1 3.5564681725 0-59 II 1 2.7789185489 0-59 II 0 5.6755646817 0-59 II 0 6.3271731691 0-59 II 0 5.5578370979 0-59 II 0 6.417522245 0-59 II 0 5.954825462 0-59 II 1 3.4113620808 0-59 II 0 5.8809034908 0-59 II 1 3.5783709788 0-59 II 1 0.7091033539 0-59 II 1 2.2861054073 0-59 II 1 0.9609856263 0-59 II 1 6.045174538 0-59 II 0 5.5633127995 0-59 II 0 5.6344969199 0-59 II 0 2.8062970568 0-59 II 0 6.0150581793 0-59 II 1 4.9801505818 0-59 II 0 5.7686516085 0-59 II 0 4.720054757 0-59 II 1 5.5633127995 0-59 II 1 2.8610540726 0-59 II 1 5.2238193018 0-59 II 0 5.0212183436 0-59 II 0 4.7939767283 0-59 II 0 5.4209445585 0-59 II 0 2.6557152635 0-59 II 0 5.067761807 0-59 II 1 2.1629021218 0-59 II 0 4.6981519507 0-59 II 0 4.9993155373 0-59 II 0 4.3394934976 0-59 II 0 4.3915126626 0-59 II 0 4.6379192334 0-59 II 0 4.7611225188 0-59 II 1 2.2970568104 0-59 II 1 1.6810403833 0-59 II 1 1.8370978782 0-59 II 1 0.7611225188 0-59 II 0 3.9780971937 0-59 II 0 3.794661191 0-59 II 0 2.6858316222 0-59 II 1 3.2361396304 0-59 II 0 3.8904859685 0-59 II 0 4.4380561259 0-59 II 0 3.9397672827 0-59 II 0 4.1889117043 0-59 II 0 3.3073237509 0-59 II 0 3.6468172485 0-59 II 0 3.9507186858 0-59 II 0 3.3483915127 0-59 II 0 3.446954141 0-59 II 0 2.6064339493 0-59 II 0 3.3620807666 0-59 II 0 3.6605065024 0-59 II 0 3.1704312115 0-59 II 0 3.3210130048 0-59 II 0 3.0061601643 0-59 II 0 3.1101984942 0-59 II 1 1.196440794 0-59 II 0 2.81724846 0-59 II 0 2.893908282 0-59 II 0 2.6338124572 0-59 II 0 2.7843942505 0-59 II 0 2.8062970568 0-59 II 0 2.2203969884 0-59 II 0 3.1375770021 0-59 II 0 2.6967830253 0-59 II 0 2.7159479808 0-59 II 0 2.507871321 0-59 II 0 2.0287474333 0-59 II 0 2.9733059548 0-59 II 0 2.1875427789 0-59 II 0 2.1464750171 0-59 II 1 2.1327857632 0-59 II 0 2.379192334 0-59 II 0 2.7488021903 0-59 II 0 2.3983572895 0-59 II 0 1.9301848049 0-59 II 1 1.2648870637 0-59 II 0 2.0889801506 0-59 II 0 2.1081451061 0-59 II 0 1.9520876112 0-59 II 0 3.0581793292 0-59 II 0 2.0506502396 0-59 II 1 0.4709103354 0-59 II 0 2.0205338809 0-59 II 0 0.3531827515 0-59 II 0 2.1245722108 0-59 II 0 2.3600273785 0-59 II 0 2.0041067762 0-59 II 0 1.8562628337 0-59 II 0 1.8945927447 0-59 II 1 1.3059548255 0-59 II 0 1.3963039014 0-59 II 0 1.2840520192 0-59 II 0 1.3661875428 0-59 II 0 1.3169062286 0-59 II 0 1.5222450376 0-59 II 0 0.9883641342 0-59 II 1 0.720054757 0-59 II 0 1.7713894593 0-59 II 0 1.1991786448 0-59 II 0 1.0924024641 0-59 II 0 1.8836413415 0-59 II 0 1.1334702259 0-59 II 0 1.0376454483 0-59 II 1 0.6652977413 0-59 II 0 1.3470225873 0-59 II 0 1.3059548255 0-59 II 0 1.1307323751 0-59 II 0 1.3963039014 0-59 II 0 1.0978781656 0-59 II 0 1.2648870637 0-59 II 0 0.7939767283 0-59 II 0 1.3689253936 0-59 II 0 1.0759753593 0-59 II 0 0.9993155373 0-59 II 0 1.2950034223 0-59 II 0 0.7501711157 0-59 II 0 1.6317590691 0-59 II 1 0.9555099247 0-59 II 0 0.3285420945 0-59 II 0 1.7248459959 0-59 II 0 1.1334702259 0-59 II 0 1.2648870637 0-59 II 0 1.3880903491 0-59 II 0 1.1937029432 0-59 II 0 0.6488706366 0-59 II 0 0.8569472964 0-59 II 0 1.0704996578 0-59 II 0 0.3367556468 0-59 II 0 0.3778234086 0-59 II 0 0.2710472279 0-59 II 0 0.2381930185 0-59 II 0 0.5722108145 0-59 II 0 0.9226557153 0-59 II 0 0.4407939767 0-59 II 0 0.4216290212 0-59 II 0 0.5393566051 0-59 II 0 0.1697467488 0-59 II 0 0.3449691992 0-59 II 0 0.3641341547 0-59 II 0 0.4462696783 0-59 II 0 0.5804243669 0-59 II 0 0.5585215606 0-59 II 0 1.9301848049 0-59 II 0 6.7789185489 0-59 II 0 5.2484599589 0-59 II 0 8.6954140999 60-69 II 0 8.2436687201 60-69 II 0 7.7125256674 60-69 II 1 0.5119780972 60-69 II 0 7.2525667351 60-69 II 0 6.5790554415 60-69 II 1 4.219028063 60-69 II 0 5.3141683778 60-69 II 0 5.3032169747 60-69 II 0 4.5366187543 60-69 II 1 4.1724845996 60-69 II 0 4.1779603012 60-69 II 0 3.5509924709 60-69 II 1 3.2361396304 60-69 II 0 3.665982204 60-69 II 1 3.6878850103 60-69 II 0 2.0588637919 60-69 II 0 2.0314852841 60-69 II 0 2.1245722108 60-69 II 0 2.0369609856 60-69 II 0 2.1382614648 60-69 II 0 1.9904175222 60-69 II 0 1.1526351814 60-69 II 0 1.0814510609 60-69 II 0 1.1362080767 60-69 II 0 1.4592744695 60-69 II 0 1.067761807 60-69 II 0 7.9698836413 60-69 II 0 6.8008213552 60-69 II 0 6.045174538 60-69 II 0 6.0150581793 60-69 II 0 5.7002053388 60-69 II 0 4.9500342231 60-69 II 0 5.7193702943 60-69 II 0 5.2484599589 60-69 II 0 2.9349760438 60-69 II 0 2.0889801506 60-69 II 0 0.1943874059 60-69 II 0 0.462696783 60-69 II 0 0.3449691992 60-69 II 0 0.3531827515 60-69 II 1 6.7926078029 60-69 II 0 7.2717316906 60-69 II 0 6.855578371 60-69 II 0 7.1074606434 60-69 II 1 7.318275154 60-69 II 0 5.3689253936 60-69 II 0 5.4045174538 60-69 II 0 5.158110883 60-69 II 0 4.7091033539 60-69 II 0 3.3894592745 60-69 II 0 4.1642710472 60-69 II 0 4.2162902122 60-69 II 0 3.8110882957 60-69 II 0 4.0054757016 60-69 II 0 2.9869952088 60-69 II 0 2.546201232 60-69 II 0 2.1409993155 60-69 II 0 2.2970568104 60-69 II 0 2.0314852841 60-69 II 0 0.9883641342 60-69 II 0 1.1416837782 60-69 II 0 1.2840520192 60-69 II 0 0.3915126626 60-69 II 0 0.3750855578 60-69 II 0 0.7227926078 60-69 II 0 8.3394934976 60-69 II 1 7.8986995209 60-69 II 1 4.7665982204 60-69 II 1 3.3237508556 60-69 II 1 7.0965092402 60-69 II 0 7.3894592745 60-69 II 1 0.7857631759 60-69 II 1 4.8350444901 60-69 II 0 6.7953456537 60-69 II 1 4.8843258042 60-69 II 1 4.59137577 60-69 II 0 6.6036960986 60-69 II 0 6.5297741273 60-69 II 0 6.0561259411 60-69 II 0 5.9247091034 60-69 II 0 6.3956194387 60-69 II 0 5.864476386 60-69 II 0 6.0424366872 60-69 II 0 6.0342231348 60-69 II 0 3.5482546201 60-69 II 1 4.3121149897 60-69 II 0 5.3853524983 60-69 II 0 4.9472963723 60-69 II 0 4.5420944559 60-69 II 1 3.8439425051 60-69 II 0 4.5229295003 60-69 II 0 4.8076659822 60-69 II 0 4.9062286105 60-69 II 0 4.4052019165 60-69 II 0 4.257357974 60-69 II 0 3.7672826831 60-69 II 1 0.7419575633 60-69 II 0 3.4661190965 60-69 II 0 3.3401779603 60-69 II 0 3.7535934292 60-69 II 1 1.3552361396 60-69 II 1 3.5017111567 60-69 II 0 2.598220397 60-69 II 0 2.7816563997 60-69 II 1 0.4380561259 60-69 II 0 2.1848049281 60-69 II 0 1.2265571526 60-69 II 0 0.9199178645 60-69 II 0 1.0102669405 60-69 II 0 1.18275154 60-69 II 0 0.5338809035 60-69 II 0 1.0951403149 60-69 II 0 0.8021902806 60-69 II 0 0.5941136208 60-69 II 0 0.3805612594 60-69 II 0 8.0438056126 60-69 II 0 6.288843258 70-79 II 0 4.733744011 70-79 II 0 5.0266940452 70-79 II 0 4.1259411362 70-79 II 0 4 70-79 II 0 3.1403148528 70-79 II 0 1.5441478439 70-79 II 0 2.1765913758 70-79 II 0 2.0561259411 70-79 II 0 1.2101300479 70-79 II 0 0.1943874059 70-79 II 1 6.2833675565 70-79 II 0 2.6173853525 70-79 II 0 4.5557837098 70-79 II 0 3.7563312799 70-79 II 0 3.1841204654 70-79 II 0 3.0335386721 70-79 II 0 1.7659137577 70-79 II 0 6.6611909651 70-79 II 0 7.0527036277 70-79 II 1 1.2457221081 70-79 II 0 5.8206707734 70-79 II 1 0.2299794661 70-79 II 1 1.5496235455 70-79 II 1 5.7577002053 70-79 II 1 0.4654346338 70-79 II 0 5.5359342916 70-79 II 0 3.8439425051 70-79 II 0 3.4250513347 70-79 II 1 2.0862422998 70-79 II 0 3.5947980835 70-79 II 0 2.7679671458 70-79 II 0 2.6255989049 70-79 II 0 3.0882956879 70-79 II 0 2.8309377139 70-79 II 0 1.9958932238 70-79 II 0 2.2231348392 70-79 II 0 0.6351813826 70-79 II 0 1.749486653 70-79 II 1 1.1033538672 70-79 II 0 1.3059548255 70-79 II 0 0.2600958248 70-79 II 0 0.3093771389 70-79 II 0 0.3121149897 70-79 II 0 7.6413415469 70-79 II 0 6.3299110198 70-79 II 1 1.87816564 70-79 II 0 5.6180698152 70-79 II 0 5.5468856947 70-79 II 0 4.7310061602 70-79 II 0 4.076659822 70-79 II 0 3.8685831622 70-79 II 0 2.8391512663 70-79 II 1 2.6119096509 70-79 II 0 2.1793292266 70-79 II 0 1.4099931554 70-79 II 0 1.067761807 70-79 II 0 1.7796030116 70-79 II 0 1.2977412731 70-79 II 0 1.1772758385 70-79 II 0 0.9582477755 70-79 II 0 1.0485968515 70-79 II 0 0.5749486653 70-79 II 0 0.4216290212 70-79 II 0 0.1149897331 70-79 II 0 0.34770705 70-79 II 0 0.3093771389 70-79 II 0 0.2464065708 70-79 II 1 3.5811088296 80+ II 0 6.7405886379 80+ II 0 4.8542094456 80+ II 1 2.045174538 80+ II 0 2.6310746064 80+ II 0 0.9527720739 80+ II 0 0.34770705 80+ II 0 2.3572895277 80+ II 0 7.3429158111 80+ II 1 0.5393566051 80+ II 0 3.1813826146 80+ II 1 1.5331964408 80+ II 0 2.7734428474 80+ II 0 3.498973306 80+ II 0 2.4777549624 80+ II 0 0.3258042437 80+ II 1 3.7754962355 80+ II 0 4.8076659822 80+ II 0 4.205338809 80+ II 0 3.3237508556 80+ II 0 1.53045859 80+ III 1 1.568788501 0-59 III 0 2.8391512663 0-59 III 0 0.7665982204 0-59 III 1 2.7022587269 0-59 III 0 5.2594113621 0-59 III 0 2.379192334 0-59 III 0 1.8206707734 0-59 III 1 0.1259411362 60-69 III 0 2.0506502396 0-59 III 0 2.9623545517 0-59 III 0 1.3935660507 0-59 III 1 2.5270362765 0-59 III 1 3.241615332 0-59 III 0 7.2689938398 70-79 III 1 2.8637919233 0-59 III 0 3.0472279261 0-59 III 1 2.0670773443 0-59 III 1 1.3004791239 0-59 III 0 5.8699520876 0-59 III 1 1.1663244353 0-59 III 1 4.8898015058 0-59 III 1 0.6023271732 0-59 III 0 4.3887748118 0-59 III 1 1.9794661191 0-59 III 0 3.5345653662 0-59 III 0 4.7255304586 0-59 III 0 4.0082135524 0-59 III 0 3.4058863792 0-59 III 0 3.8795345654 0-59 III 0 2.9897330595 0-59 III 1 1.8316221766 0-59 III 0 3.9151266256 0-59 III 0 2.3490759754 0-59 III 0 2.1711156742 0-59 III 1 0.9253935661 0-59 III 1 1.1115674196 0-59 III 1 1.3059548255 0-59 III 0 2.6529774127 0-59 III 0 1.4264202601 0-59 III 0 1.6783025325 0-59 III 0 0.205338809 0-59 III 1 2.4476386037 0-59 III 0 6.0232717317 0-59 III 1 4.9500342231 0-59 III 0 3.7097878166 0-59 III 0 4.5448323066 0-59 III 1 3.2224503765 0-59 III 0 3.8877481177 0-59 III 0 2.6338124572 0-59 III 0 2.726899384 0-59 III 0 2.0287474333 0-59 III 0 1.8973305955 0-59 III 1 1.1033538672 0-59 III 0 1.5523613963 0-59 III 1 0.9281314168 0-59 III 0 0.2464065708 0-59 III 0 0.553045859 0-59 III 0 8.2080766598 0-59 III 0 8.1642710472 0-59 III 0 7.4496919918 0-59 III 0 7.7125256674 0-59 III 0 7.5646817248 0-59 III 1 4.4873374401 0-59 III 1 1.7056810404 0-59 III 1 1.568788501 0-59 III 0 7.2251882272 0-59 III 1 1.2402464066 0-59 III 1 1.8042436687 0-59 III 1 1.6098562628 0-59 III 0 5.3305954825 0-59 III 1 1.0622861054 0-59 III 1 2.1464750171 0-59 III 0 5.3086926762 0-59 III 1 0.7474332649 0-59 III 1 2.9815195072 0-59 III 1 2.5790554415 0-59 III 0 5.3388090349 0-59 III 1 1.4510609172 0-59 III 1 1.8672142368 0-59 III 0 4.0191649555 0-59 III 0 4.1724845996 0-59 III 1 2.340862423 0-59 III 1 2.6420260096 0-59 III 1 2.5215605749 0-59 III 0 4.1259411362 0-59 III 1 1.4976043806 0-59 III 0 3.6440793977 0-59 III 0 3.099247091 0-59 III 1 2.2368240931 0-59 III 1 2.8090349076 0-59 III 0 2.3682409309 0-59 III 1 2.1765913758 0-59 III 0 1.9739904175 0-59 III 0 2.4120465435 0-59 III 0 2.5681040383 0-59 III 1 0.8377823409 0-59 III 1 1.6427104723 0-59 III 0 1.8453114305 0-59 III 0 1.6892539357 0-59 III 1 0.9445585216 0-59 III 0 1.5797399042 0-59 III 1 1.6919917864 0-59 III 0 1.9876796715 0-59 III 0 2.636550308 0-59 III 0 1.5934291581 0-59 III 0 1.1006160164 0-59 III 0 0.4818617385 0-59 III 0 1.6563997262 0-59 III 0 0.900752909 0-59 III 1 1.1088295688 0-59 III 1 0.5749486653 0-59 III 1 1.1937029432 0-59 III 0 1.2813141684 0-59 III 0 1.3004791239 0-59 III 0 1.3223819302 0-59 III 0 0.7145790554 0-59 III 0 0.3613963039 0-59 III 0 0.3997262149 0-59 III 0 0.4572210815 0-59 III 0 0.6543463381 0-59 III 0 0.2162902122 0-59 III 0 1.2073921971 60-69 III 0 1.2703627652 60-69 III 0 2.5106091718 60-69 III 0 2.3162217659 60-69 III 0 1.106091718 60-69 III 0 0.2217659138 60-69 III 0 7.5318275154 60-69 III 1 1.7002053388 60-69 III 0 5.3059548255 60-69 III 1 4.1095140315 60-69 III 1 2.1026694045 60-69 III 0 2.7789185489 60-69 III 0 2.8227241615 60-69 III 0 2.4394250513 60-69 III 0 2.0424366872 60-69 III 0 1.9575633128 60-69 III 0 1.2648870637 60-69 III 1 1.5715263518 60-69 III 0 4.8651608487 60-69 III 0 7.8959616701 70-79 III 1 3.9315537303 70-79 III 1 1.1389459274 70-79 III 0 0.5941136208 80+ III 0 0.0958247775 80+ III 0 0.7802874743 80+ III 0 0.4407939767 80+ IV 1 1.9849418207 0-59 IV 1 2.6036960986 0-59 IV 1 0.9938398357 0-59 IV 1 0.2710472279 0-59 IV 1 0.0492813142 0-59 IV 1 4.2847364819 0-59 IV 1 3.1759069131 0-59 IV 0 0.3121149897 0-59 IV 0 8.3422313484 60-69 IV 0 2.8090349076 60-69 IV 1 0.8569472964 60-69 IV 1 4.9336071184 70-79 IV 0 4.2245037645 0-59 IV 0 1.3278576318 0-59 IV 1 2.4120465435 0-59 IV 1 1.2785763176 0-59 IV 1 5.0047912389 0-59 IV 1 1.7522245038 0-59 IV 0 0.6789869952 0-59 IV 0 0.2299794661 0-59 IV 0 1.0075290897 0-59 IV 0 0.4435318275 0-59 IV 0 0.3997262149 0-59 IV 0 2.6639288159 0-59 IV 0 0.4681724846 0-59 IV 1 0.1368925394 0-59 IV 1 4.2464065708 0-59 IV 1 2.6255989049 0-59 IV 0 2.4558521561 0-59 IV 1 0.0301163587 0-59 IV 0 0.4216290212 0-59 IV 1 0.1095140315 0-59 IV 1 6.3737166324 0-59 IV 1 3.8850102669 0-59 IV 1 1.6372347707 0-59 IV 1 0.4106776181 0-59 IV 1 1.1690622861 0-59 IV 1 0.7885010267 0-59 IV 1 1.4839151266 0-59 IV 1 2.1765913758 0-59 IV 1 2.4257357974 0-59 IV 1 1.1197809719 0-59 IV 1 0.2354551677 0-59 IV 1 1.2840520192 0-59 IV 1 1.9329226557 0-59 IV 0 2.3737166324 0-59 IV 1 0.0027378508 0-59 IV 1 0.7912388775 0-59 IV 1 1.2594113621 0-59 IV 1 0.1943874059 0-59 IV 0 1.7029431896 0-59 IV 0 1.0485968515 0-59 IV 0 0.386036961 0-59 IV 0 0.8514715948 0-59 IV 0 1.0568104038 0-59 IV 0 0.334017796 0-59 IV 0 0.3805612594 0-59 IV 0 0.4462696783 0-59 IV 0 2.1108829569 60-69 IV 0 1.3880903491 60-69 IV 0 0.476386037 60-69 IV 0 0.4188911704 60-69 IV 1 1.9411362081 60-69 IV 1 1.9603011636 60-69 IV 1 1.0485968515 60-69 IV 1 2.0643394935 60-69 IV 1 1.6262833676 60-69 IV 1 3.6002737851 60-69 IV 1 0.5968514716 60-69 IV 0 1.1307323751 60-69 IV 1 0.1368925394 60-69 IV 0 0.3586584531 60-69 IV 0 0.090349076 60-69 IV 0 0.4298425736 60-69 IV 1 4.1533196441 60-69 IV 1 3.5044490075 70-79 IV 0 3.7782340862 70-79 IV 0 2.0862422998 70-79 IV 1 0.9993155373 70-79 IV 1 0.1752224504 70-79 IV 1 0.5393566051 70-79 IV 1 0.4955509925 70-79 IV 1 0.2874743326 80+ IV 1 1.7604380561 80+ IV 1 4.7994524298 80+ IV 0 1.2128678987 80+ IV 0 0.7310061602 80+ ; *************************************************** /* The following data set is needed for exercise */ /* 3.1.1.4 on page 83. */ **************************************************** data; input Time id state; datalines; 0 1 1 49 1 3 0 2 1 5 2 3 0 3 1 0 3 2 15 3 3 0 4 1 35 4 2 38 4 3 0 5 1 17 5 3 0 6 1 2 6 3 0 7 1 50 7 2 674 7 3 0 8 1 39 8 3 0 9 1 84 9 3 0 10 1 11 10 2 57 10 3 0 11 1 25 11 2 152 11 3 0 12 1 7 12 3 0 13 1 16 13 2 80 13 3 0 14 1 36 14 2 1386 14 3 0 16 1 27 16 2 307 16 3 0 17 1 35 17 3 0 18 1 19 18 2 42 18 3 0 19 1 36 19 3 0 20 1 17 20 2 27 20 3 0 21 1 7 21 2 1031 21 3 0 22 1 11 22 2 50 22 3 0 23 1 2 23 2 732 23 3 0 24 1 82 24 2 218 24 3 0 25 1 24 25 2 1799 25 0 0 26 1 1400 26 0 0 27 1 262 27 3 0 28 1 70 28 2 71 28 3 0 29 1 34 29 3 0 30 1 15 30 2 851 30 3 0 31 1 15 31 3 0 32 1 16 32 2 76 32 3 0 33 1 50 33 2 1586 33 0 0 34 1 22 34 2 1571 34 0 0 35 1 11 35 3 0 36 1 45 36 2 99 36 3 0 37 1 18 37 2 65 37 3 0 38 1 4 38 2 4 38 3 0 39 1 1 39 2 52 39 3 0 40 1 40 40 2 1407 40 0 0 41 1 57 41 2 1321 41 0 0 42 1 2 42 3 0 43 1 1 43 3 0 44 1 39 44 3 0 45 1 0 45 2 44 45 3 0 46 1 1 46 2 995 46 3 0 47 1 20 47 2 71 47 3 0 48 1 8 48 3 0 49 1 35 49 2 1141 49 0 0 50 1 82 50 2 979 50 3 0 51 1 31 51 2 284 51 3 0 52 1 101 52 3 0 53 1 40 53 2 187 53 3 0 54 1 2 54 3 0 55 1 9 55 2 60 55 3 0 56 1 66 56 2 941 56 0 0 57 1 148 57 3 0 58 1 20 58 2 342 58 3 0 59 1 77 59 2 915 59 0 0 60 1 2 60 2 52 60 3 0 61 1 1 61 3 0 62 1 68 62 3 0 63 1 26 63 2 841 63 0 0 64 1 32 64 2 583 64 3 0 65 1 11 65 2 77 65 3 0 66 1 31 66 3 0 67 1 56 67 2 284 67 3 0 68 1 2 68 2 67 68 3 0 69 1 9 69 2 669 69 0 0 70 1 4 70 2 29 70 3 0 71 1 30 71 2 619 71 0 0 72 1 3 72 2 595 72 0 0 73 1 26 73 2 89 73 3 0 74 1 4 74 2 16 74 3 0 75 1 1 75 3 0 76 1 45 76 2 544 76 0 0 77 1 20 77 3 0 78 1 209 78 2 514 78 0 0 79 1 66 79 2 95 79 3 0 80 1 25 80 2 481 80 0 0 81 1 5 81 2 444 81 0 0 82 1 427 82 0 0 83 1 31 83 2 79 83 3 0 84 1 36 84 2 333 84 3 0 85 1 4 85 3 0 86 1 7 86 2 396 86 0 0 87 1 59 87 2 109 87 3 0 88 1 30 88 2 369 88 0 0 89 1 138 89 2 206 89 3 0 90 1 159 90 2 185 90 3 0 91 1 339 91 3 0 92 1 309 92 2 339 92 0 0 93 1 27 93 2 264 93 0 0 94 1 3 94 2 164 94 3 0 95 1 1 95 2 15 95 3 0 96 1 12 96 2 179 96 0 0 97 1 20 97 2 130 97 0 0 98 1 95 98 2 108 98 0 0 99 1 20 99 3 0 100 1 37 100 2 38 100 0 0 101 1 30 101 0 0 102 1 10 102 0 0 103 1 5 103 3 ; **************************************************** /* The following data set is mentioned on page 118,*/ /* where it is used to produce outputs 4.1 - 4.4, */ /* and 4.6 - 4.7, and figure 4.1 - 4.2. */ /* This dataset originally included the dates of */ /* biopsy and last followup. Since it is no longer*/ /* considered appropriate to publish such data, it */ /* is now omitted and the variable month which */ /* equals (followup - biopsy)/30.4 is given. */ **************************************************** data; input clarklevel $ ulceration $ thickness status $ months; datalines; V No 20 Dead 10.394736842 V Yes 13.5 Alive 36.282894737 V Yes 13 Alive 36.447368421 V Yes 13 Alive 1.3815789474 IV Yes 12.6 Alive 1.3486842105 IV Yes 12.1 Alive 0.9210526316 V Yes 12 Alive 11.282894737 V Yes 11.64 Alive 17.861842105 V Yes 11 Alive 27.006578947 UNK Unknown 11 Alive 2.2697368421 V Unknown 11 Alive 34.967105263 V Yes 11 Alive 26.907894737 V Unknown 11 Alive 5.5263157895 UNK Unknown 10.6 Alive 3.125 IV No 10.2 Alive 14.013157895 IV Yes 10 Alive 0.4276315789 V Unknown 10 Alive 1.0855263158 IV Yes 10 Alive 0.4934210526 V Unknown 10 Alive 22.006578947 IV Yes 10 Alive 18.980263158 V No 9.5 Alive 51.151315789 V No 9 Alive 56.710526316 V No 9 Dead 30.098684211 IV Yes 9 Alive 5.4276315789 IV No 9 Alive 9.2434210526 V Yes 8.88 Alive 13.157894737 V Yes 8.7 Alive 6.6776315789 V Yes 8.5 Alive 7.2039473684 IV Yes 8.2 Alive 9.5723684211 V Yes 8.1 Alive 5.1973684211 IV Yes 8.1 Alive 16.217105263 V Yes 8 Alive 8.7828947368 V Unknown 8 Alive 66.381578947 V Yes 8 Alive 8.5526315789 V No 8 Alive 11.480263158 V Yes 8 Alive 1.4144736842 IV Yes 7.6 Alive 20.427631579 V Unknown 7.5 Dead 14.802631579 IV Unknown 7.4 Alive 44.407894737 V Yes 7.4 Dead 19.440789474 IV Unknown 7.4 Dead 18.388157895 IV Yes 7.25 Alive 0.3618421053 III Unknown 7.1 Alive 20.263157895 IV Unknown 7.05 Alive 64.243421053 UNK Unknown 7 Alive 56.217105263 V Yes 7 Dead 24.703947368 V No 7 Dead 31.578947368 V Yes 7 Dead 26.611842105 III No 7 Alive 2.4671052632 IV Yes 7 Dead 1.5789473684 IV Unknown 6.9 Alive 19.078947368 V Yes 6.87 Alive 1.6776315789 IV Yes 6.8 Alive 43.355263158 V No 6.75 Alive 2.2697368421 IV Yes 6.7 Dead 10.789473684 V Unknown 6.5 Alive 19.375 V No 6.5 Dead 17.763157895 IV Yes 6.5 Alive 4.0131578947 V Yes 6.47 Alive 26.019736842 IV Yes 6.2 Alive 1.1513157895 IV Yes 6 Alive 72.927631579 III Yes 6 Dead 15.032894737 V No 6 Dead 14.276315789 V Yes 6 Alive 65.394736842 V Yes 6 Alive 18.815789474 UNK Yes 6 Alive 25.526315789 IV No 6 Alive 13.980263158 IV Yes 6 Alive 22.894736842 IV No 6 Alive 4.5723684211 V Yes 6 Alive 18.355263158 IV No 6 Alive 11.217105263 IV No 6 Alive 8.3223684211 V No 5.94 Alive 1.5460526316 IV Yes 5.8 Alive 9.6710526316 IV Unknown 5.8 Alive 65.230263158 IV Yes 5.75 Alive 2.4671052632 V Unknown 5.7 Alive 22.796052632 V No 5.6 Alive 13.289473684 IV Yes 5.55 Alive 24.276315789 IV Yes 5.5 Dead 8.75 III No 5.5 Alive 55.197368421 IV Yes 5.5 Dead 18.881578947 IV Yes 5.5 Alive 31.480263158 IV Yes 5.5 Alive 0.625 IV Yes 5.5 Dead 32.993421053 IV No 5.5 Alive 17.302631579 IV Unknown 5.5 Alive 24.473684211 IV No 5.5 Alive 56.019736842 V Unknown 5.4 Dead 14.013157895 IV No 5.4 Alive 15.822368421 IV Yes 5.4 Alive 30.131578947 IV Unknown 5.38 Alive 2.5 IV No 5.3 Alive 84.309210526 IV Unknown 5.3 Alive 20.394736842 IV No 5.3 Alive 19.671052632 IV Yes 5.2 Alive 36.447368421 IV Yes 5.2 Alive 22.467105263 IV Yes 5.2 Alive 12.861842105 IV Yes 5.2 Alive 26.546052632 IV Yes 5.12 Alive 18.453947368 IV Yes 5.12 Alive 5.9210526316 IV No 5.1 Alive 24.375 V No 5.1 Alive 44.572368421 IV Unknown 5.1 Alive 42.302631579 IV Unknown 5.1 Alive 3.8815789474 IV No 5.05 Alive 46.907894737 IV Yes 5.03 Alive 14.276315789 IV Unknown 5 Dead 13.75 IV Yes 5 Dead 11.611842105 IV No 5 Alive 34.605263158 IV Yes 5 Dead 48.947368421 IV Yes 5 Alive 9.5723684211 IV Yes 5 Dead 11.776315789 V Yes 5 Alive 0.5921052632 IV Unknown 5 Dead 38.618421053 UNK Unknown 5 Dead 12.269736842 V Yes 5 Alive 32.664473684 IV Yes 5 Alive 14.605263158 IV Yes 5 Alive 10.855263158 IV Yes 5 Alive 10.065789474 IV Yes 5 Alive 8.7171052632 IV Yes 5 Dead 20.822368421 V No 4.98 Alive 22.302631579 IV Yes 4.91 Dead 23.651315789 IV Yes 4.91 Alive 16.743421053 IV No 4.87 Alive 2.3026315789 IV Yes 4.85 Alive 17.861842105 IV Yes 4.8 Dead 22.401315789 IV Yes 4.8 Alive 31.25 V Yes 4.8 Alive 14.111842105 IV Yes 4.75 Alive 24.572368421 IV Yes 4.7 Alive 28.585526316 V No 4.68 Alive 89.111842105 V Yes 4.62 Alive 14.967105263 III Yes 4.6 Dead 12.763157895 IV Yes 4.6 Alive 1.1842105263 V No 4.59 Alive 0.3947368421 IV Yes 4.55 Alive 32.269736842 IV No 4.55 Alive 10.592105263 IV Yes 4.5 Dead 22.631578947 IV Yes 4.5 Alive 3.2565789474 IV Yes 4.5 Alive 55.361842105 IV Yes 4.5 Alive 18.848684211 IV Unknown 4.5 Alive 12.532894737 IV Yes 4.5 Alive 0.6907894737 V No 4.5 Alive 20.592105263 IV Yes 4.5 Alive 5.9868421053 IV No 4.42 Alive 32.203947368 IV Yes 4.4 Alive 13.848684211 IV Unknown 4.4 Alive 0.8552631579 IV No 4.34 Alive 14.046052632 IV Unknown 4.3 Alive 58.322368421 IV Unknown 4.3 Alive 26.710526316 V No 4.29 Alive 0.4605263158 V Yes 4.22 Alive 46.447368421 IV Yes 4.2 Dead 18.519736842 IV No 4.2 Alive 15.394736842 III Yes 4.2 Alive 11.743421053 V Yes 4.18 Alive 5.3289473684 IV Yes 4.15 Dead 36.019736842 IV Yes 4.13 Alive 9.7697368421 UNK Yes 4.1 Alive 39.769736842 IV No 4.1 Alive 58.421052632 IV Yes 4.1 Alive 4.9671052632 IV No 4.1 Alive 9.5065789474 IV Yes 4.1 Alive 16.776315789 V Yes 4.1 Alive 9.1776315789 IV Yes 4 Alive 1.5131578947 III Yes 4 Dead 35.625 V Unknown 4 Dead 55 IV Yes 4 Dead 11.907894737 IV Yes 4 Dead 23.026315789 IV No 4 Alive 32.697368421 III Yes 4 Alive 31.611842105 IV Yes 4 Alive 30.723684211 IV No 4 Alive 28.486842105 IV Unknown 4 Alive 11.677631579 IV No 4 Dead 23.026315789 IV Unknown 4 Alive 22.598684211 IV Yes 4 Alive 20.526315789 III Unknown 4 Alive 42.960526316 V No 4 Alive 5.5921052632 IV Yes 4 Alive 8.75 III Unknown 4 Alive 23.881578947 IV Unknown 4 Alive 16.184210526 UNK Yes 3.9 Dead 19.934210526 V Yes 3.9 Alive 51.776315789 IV No 3.89 Alive 34.671052632 IV Yes 3.87 Alive 10.855263158 IV No 3.86 Alive 1.5789473684 IV Yes 3.86 Alive 15.690789474 IV Unknown 3.83 Alive 4.4407894737 UNK Yes 3.8 Alive 27.993421053 V Yes 3.8 Alive 47.203947368 IV Yes 3.8 Alive 0.4934210526 V Yes 3.8 Alive 13.717105263 III No 3.8 Dead 71.151315789 UNK No 3.8 Alive 1.6118421053 IV No 3.75 Alive 23.388157895 V Yes 3.75 Dead 17.730263158 IV Yes 3.75 Alive 33.618421053 V No 3.7 Dead 36.217105263 IV Yes 3.7 Dead 20.230263158 IV Unknown 3.7 Alive 42.828947368 IV Unknown 3.7 Alive 1.8421052632 IV Unknown 3.7 Alive 8.4210526316 III Yes 3.7 Alive 7.7960526316 IV No 3.64 Alive 5.5263157895 IV Yes 3.64 Alive 17.072368421 IV Yes 3.63 Alive 17.565789474 III No 3.63 Alive 6.5460526316 IV Yes 3.6 Alive 37.894736842 IV Unknown 3.6 Dead 54.802631579 IV Unknown 3.6 Dead 25.822368421 IV Yes 3.6 Alive 26.480263158 IV Yes 3.6 Alive 4.6381578947 V Yes 3.55 Dead 2.0723684211 IV No 3.5 Alive -0.032894737 IV No 3.5 Alive 5.7565789474 UNK Yes 3.5 Alive 40.131578947 IV No 3.5 Alive 6.5789473684 IV Yes 3.5 Dead 2.4013157895 IV Unknown 3.5 Dead 25.756578947 III No 3.5 Alive 9.7368421053 III Yes 3.5 Dead 17.993421053 IV Yes 3.5 Alive 4.9671052632 IV Yes 3.5 Alive 20.559210526 III Yes 3.5 Alive 20.690789474 UNK Unknown 3.5 Alive 0.6578947368 IV Yes 3.5 Alive 5.625 IV No 3.48 Alive 8.7828947368 IV Yes 3.45 Dead 15.855263158 III No 3.41 Alive 13.486842105 IV No 3.4 Dead 17.006578947 V Yes 3.4 Alive 49.934210526 UNK Unknown 3.4 Alive 31.184210526 IV Unknown 3.4 Alive 20.592105263 V Unknown 3.4 Alive 17.302631579 IV No 3.4 Alive 13.355263158 IV Yes 3.4 Alive 12.993421053 III Yes 3.4 Alive 3.9473684211 IV No 3.37 Alive 3.0263157895 IV Yes 3.35 Alive 12.697368421 IV No 3.3 Alive . V Unknown 3.3 Alive 58.256578947 IV Unknown 3.3 Alive 24.802631579 IV Yes 3.3 Alive 18.782894737 IV No 3.3 Alive 0.4276315789 IV Yes 3.3 Alive 5.3289473684 IV No 3.28 Alive 21.381578947 IV Yes 3.25 Dead 18.322368421 IV Yes 3.25 Alive 13.125 IV Yes 3.23 Alive 14.967105263 IV Yes 3.21 Alive 5.0986842105 IV Yes 3.2 Dead 1.1513157895 IV Unknown 3.2 Alive . IV Unknown 3.2 Alive 36.414473684 IV No 3.2 Alive 5.1315789474 IV Yes 3.2 Alive 0.4605263158 IV No 3.19 Alive 9.7368421053 IV Yes 3.17 Alive 0 III No 3.17 Alive 24.440789474 IV Yes 3.16 Alive 4.1776315789 IV Yes 3.16 Alive 10.296052632 V Yes 3.15 Alive 6.9736842105 III Unknown 3.13 Alive 76.315789474 V Yes 3.13 Alive 14.671052632 IV No 3.12 Alive 8.3881578947 IV Yes 3.1 Dead 6.8421052632 IV No 3.1 Alive 41.513157895 UNK Unknown 3.1 Dead 14.407894737 IV Yes 3.1 Alive 18.388157895 IV Unknown 3.1 Alive 40.361842105 IV Yes 3.1 Alive 12.631578947 IV No 3.1 Alive 2.0394736842 IV No 3.08 Dead 22.006578947 III Unknown 3.08 Alive 20.756578947 IV Yes 3.03 Dead 8.9802631579 IV Unknown 3.01 Alive 13.585526316 IV Yes 3.01 Alive 16.940789474 IV Yes 3 Alive 22.598684211 IV Unknown 3 Dead 23.322368421 IV Unknown 3 Alive 21.973684211 IV Unknown 3 Alive 37.631578947 IV Yes 3 Dead 20.723684211 III No 3 Alive 26.480263158 IV Yes 3 Alive 49.078947368 IV Unknown 3 Alive 46.052631579 IV Unknown 3 Alive 28.256578947 UNK No 3 Alive 23.980263158 IV Yes 3 Alive 48.881578947 IV Yes 3 Alive 37.763157895 IV Yes 3 Alive 14.605263158 IV Yes 3 Dead 15.394736842 II Yes 3 Alive 3.2236842105 III Yes 3 Alive 19.342105263 IV Yes 3 Alive 2.3355263158 IV Unknown 3 Alive 1.0855263158 IV Yes 3 Alive 0.4605263158 IV Yes 3 Alive 4.375 IV No 3 Alive 14.901315789 III Yes 3 Alive 12.993421053 V Yes 3 Alive 54.835526316 IV Yes 3 Alive 0.9210526316 IV Yes 3 Alive 10.361842105 IV Unknown 3 Alive 3.9473684211 IV No 3 Alive 5.8223684211 IV No 3 Alive 2.6973684211 III Unknown 3 Alive 1.1184210526 IV Yes 3 Alive 39.243421053 V No 3 Alive 6.2828947368 IV Unknown 2.92 Alive 17.993421053 IV Unknown 2.92 Alive 4.4078947368 III Yes 2.91 Alive 53.914473684 IV Unknown 2.9 Alive . IV Yes 2.9 Alive 32.631578947 IV No 2.9 Alive 47.434210526 IV Unknown 2.9 Alive 42.039473684 IV Unknown 2.9 Alive 36.184210526 IV Yes 2.9 Dead 21.414473684 III Yes 2.89 Alive 17.894736842 IV Yes 2.89 Alive 10.361842105 IV Unknown 2.89 Alive 5.5921052632 IV No 2.88 Alive 13.289473684 V No 2.87 Alive 12.171052632 III Yes 2.86 Alive 44.078947368 IV No 2.84 Alive 61.677631579 IV No 2.82 Alive 15.493421053 III Unknown 2.8 Alive 10.328947368 IV No 2.8 Alive 37.302631579 IV Unknown 2.8 Alive 0.2302631579 IV Yes 2.8 Alive 1.9736842105 III Unknown 2.8 Dead 50.230263158 V No 2.8 Dead 8.0921052632 V Unknown 2.8 Alive 29.144736842 IV Yes 2.8 Alive 35.230263158 IV Yes 2.8 Alive 42.861842105 IV No 2.8 Alive 36.184210526 III No 2.8 Alive 31.085526316 IV Yes 2.8 Alive 23.651315789 IV Unknown 2.8 Alive 0.5921052632 IV Unknown 2.8 Alive 15.361842105 IV Unknown 2.8 Alive 20.756578947 IV No 2.8 Alive 8.6842105263 III Yes 2.8 Dead 8.4868421053 IV Yes 2.77 Alive 70.394736842 IV Yes 2.77 Dead 22.631578947 IV Yes 2.75 Alive 86.315789474 IV No 2.75 Alive 22.828947368 IV Yes 2.75 Alive 24.210526316 IV Yes 2.75 Alive 33.552631579 IV Yes 2.75 Alive 5.5592105263 IV Yes 2.75 Alive 5.5921052632 IV Yes 2.75 Alive 5.8881578947 IV Yes 2.74 Alive 15.855263158 IV Yes 2.73 Alive 50.263157895 IV Yes 2.7 Dead 74.736842105 UNK No 2.7 Alive 60.559210526 IV Unknown 2.7 Alive 38.421052632 IV Unknown 2.7 Alive 83.223684211 III Yes 2.7 Dead 9.0131578947 IV No 2.7 Alive 25.131578947 IV No 2.7 Alive 31.282894737 IV No 2.7 Alive 10.065789474 IV No 2.64 Alive 46.710526316 III No 2.64 Alive 40.065789474 III Yes 2.63 Alive 40.986842105 V No 2.62 Alive 36.414473684 IV No 2.6 Dead 41.217105263 IV Unknown 2.6 Dead 40.065789474 IV No 2.6 Dead 50.690789474 III No 2.6 Alive 49.506578947 IV Yes 2.6 Alive 48.717105263 IV No 2.6 Alive 1.0197368421 IV No 2.6 Alive 48.618421053 IV Yes 2.6 Alive 19.868421053 IV Yes 2.6 Alive 7.2697368421 IV No 2.6 Alive 13.026315789 IV No 2.6 Alive 4.6710526316 IV Unknown 2.58 Alive 18.421052632 III Unknown 2.57 Dead 27.335526316 IV Yes 2.57 Alive 20.888157895 IV No 2.55 Alive 47.730263158 IV No 2.55 Alive 39.835526316 IV No 2.55 Alive 16.809210526 IV Yes 2.55 Alive 0.4934210526 III Unknown 2.55 Alive 37.039473684 III Yes 2.55 Alive 39.177631579 IV No 2.55 Alive 2.5986842105 IV Unknown 2.55 Alive 14.013157895 IV No 2.54 Dead 48.848684211 IV No 2.54 Alive 9.7697368421 IV Yes 2.52 Alive 3.1907894737 III Unknown 2.5 Alive 24.046052632 III No 2.5 Alive 1.5460526316 IV No 2.5 Alive 68.355263158 III Yes 2.5 Alive 65.592105263 IV Unknown 2.5 Dead 5.8881578947 IV Yes 2.5 Alive 2.5657894737 IV Unknown 2.5 Dead 15.032894737 IV Unknown 2.5 Alive 0.6907894737 IV Yes 2.5 Alive 26.875 IV No 2.5 Alive 7.5986842105 IV Yes 2.5 Alive 18.651315789 IV No 2.5 Alive 14.736842105 IV Yes 2.5 Alive 14.506578947 III Unknown 2.5 Alive 0.3947368421 III Unknown 2.5 Alive 39.243421053 IV Yes 2.5 Alive 14.309210526 IV Yes 2.5 Alive 17.993421053 IV Yes 2.5 Alive 43.059210526 V Yes 2.5 Alive 3.8815789474 IV Unknown 2.5 Alive 6.875 IV No 2.5 Alive 7.8618421053 IV No 2.5 Alive 28.486842105 III Yes 2.5 Alive 26.25 IV No 2.49 Dead 13.75 IV No 2.46 Alive 18.059210526 IV No 2.45 Alive 14.769736842 IV No 2.45 Alive 9.2105263158 IV Yes 2.44 Dead 19.243421053 IV No 2.44 Alive 0.4605263158 III Yes 2.4 Dead 28.684210526 IV Unknown 2.4 Dead 31.480263158 IV Yes 2.4 Alive 4.6381578947 IV Yes 2.4 Dead 33.486842105 IV No 2.4 Alive 7.8289473684 IV No 2.4 Alive 37.763157895 IV No 2.4 Alive 66.743421053 IV Unknown 2.4 Alive 8.7171052632 IV No 2.4 Alive 20.986842105 III Yes 2.4 Dead 68.092105263 IV No 2.4 Alive 15.690789474 IV No 2.38 Dead 29.276315789 IV Yes 2.38 Alive 21.546052632 IV Yes 2.35 Alive 7.9276315789 IV Yes 2.35 Alive 5.0328947368 IV Yes 2.34 Alive 41.907894737 IV Yes 2.34 Alive 54.407894737 III Yes 2.32 Alive 36.151315789 IV Yes 2.32 Alive 15.394736842 IV Yes 2.31 Alive 30.296052632 IV Yes 2.3 Dead 66.25 IV No 2.3 Dead 39.078947368 III Yes 2.3 Dead 16.644736842 III Unknown 2.3 Alive 45.888157895 IV Yes 2.3 Alive 14.407894737 IV No 2.3 Alive 34.703947368 IV Unknown 2.3 Alive 63.289473684 III Yes 2.3 Dead 27.730263158 III Unknown 2.3 Alive 45.032894737 III No 2.3 Alive 22.861842105 IV Unknown 2.3 Alive 19.210526316 IV No 2.3 Alive 97.894736842 V Unknown 2.3 Alive 19.407894737 IV Yes 2.3 Alive 14.046052632 IV No 2.3 Alive 5.3618421053 IV Yes 2.3 Alive 54.111842105 IV No 2.3 Alive 56.25 IV Yes 2.3 Alive 45.032894737 IV Unknown 2.28 Dead 64.605263158 IV No 2.28 Alive 25.855263158 IV No 2.27 Alive 68.157894737 III Unknown 2.25 Alive 7.3684210526 IV No 2.25 Alive 17.763157895 IV Yes 2.25 Alive 10.361842105 V No 2.25 Alive 13.256578947 III No 2.25 Alive 11.480263158 UNK Unknown 2.25 Alive 0.2960526316 IV Unknown 2.25 Alive 5.3289473684 IV No 2.24 Alive 42.138157895 IV Yes 2.24 Alive 6.4473684211 IV Yes 2.23 Alive 0.4605263158 IV No 2.2 Alive 19.276315789 IV Yes 2.2 Alive 67.072368421 III Yes 2.2 Alive 61.743421053 IV Yes 2.2 Alive 46.776315789 IV Yes 2.2 Alive 4.1447368421 IV Yes 2.2 Dead 13.190789474 IV No 2.2 Alive 38.552631579 III Yes 2.2 Dead 34.539473684 III Yes 2.2 Alive 0.2631578947 III Unknown 2.2 Alive 22.302631579 III No 2.2 Alive -8.256578947 IV Unknown 2.2 Alive 2.4013157895 IV Unknown 2.2 Dead 23.421052632 IV No 2.2 Alive 25.131578947 IV No 2.2 Alive 13.256578947 IV No 2.2 Alive 11.052631579 IV No 2.2 Alive 21.085526316 IV Unknown 2.2 Alive 6.0855263158 IV No 2.2 Alive 10.098684211 III Yes 2.19 Alive 53.092105263 IV No 2.16 Alive 2.4013157895 IV No 2.15 Dead 42.993421053 III Yes 2.15 Dead 30 III Yes 2.12 Alive 7.6644736842 IV No 2.12 Alive 26.842105263 IV Yes 2.11 Alive 23.947368421 III No 2.1 Dead 67.006578947 IV No 2.1 Alive . IV No 2.1 Alive 57.927631579 IV Unknown 2.1 Alive 30.263157895 III Unknown 2.1 Alive 36.875 IV Unknown 2.1 Alive 35.690789474 IV Unknown 2.1 Alive 29.901315789 IV No 2.1 Alive 36.184210526 II Unknown 2.1 Alive . IV No 2.1 Alive 31.381578947 IV No 2.1 Alive 11.940789474 IV No 2.1 Alive 23.914473684 IV No 2.1 Alive 1.3157894737 II No 2.1 Alive 20.230263158 IV No 2.1 Alive 1.6118421053 IV Yes 2.1 Alive 12.434210526 IV No 2.1 Alive 22.828947368 IV Yes 2.1 Alive 10.921052632 IV No 2.05 Alive 49.111842105 IV Yes 2.05 Alive . III Yes 2.05 Alive 8.8815789474 IV No 2.05 Alive 10.592105263 IV No 2.04 Alive 1.875 IV No 2.03 Alive 7.4671052632 IV No 2.02 Alive 34.375 IV No 2 Alive 81.875 IV Unknown 2 Alive . IV No 2 Dead 37.171052632 IV No 2 Alive 35.394736842 IV No 2 Alive 0.4605263158 IV No 2 Alive 62.401315789 III No 2 Alive . III No 2 Alive 11.973684211 IV No 2 Alive 58.322368421 IV No 2 Alive 14.802631579 IV Unknown 2 Alive 11.611842105 III No 2 Alive 38.059210526 IV No 2 Alive 42.401315789 IV Unknown 2 Alive 29.046052632 II Yes 2 Alive 34.078947368 IV No 2 Alive 46.348684211 IV Unknown 2 Alive 23.256578947 III No 2 Alive 0.4934210526 IV No 2 Alive 10.953947368 IV No 2 Alive 3.0592105263 IV Yes 2 Alive 0.9210526316 III Unknown 1.98 Dead 18.092105263 IV Yes 1.96 Alive 21.282894737 IV Yes 1.95 Dead 26.480263158 III No 1.95 Alive 54.144736842 IV Unknown 1.95 Alive 24.901315789 IV No 1.95 Alive 1.0855263158 III Yes 1.95 Alive 8.0592105263 IV Unknown 1.93 Alive 22.105263158 IV Yes 1.92 Alive 9.375 III No 1.9 Alive 57.828947368 IV Yes 1.9 Alive 25.526315789 UNK Yes 1.9 Alive 35.690789474 IV Unknown 1.9 Alive 10.460526316 UNK Yes 1.9 Alive 34.046052632 IV No 1.89 Alive 14.309210526 II No 1.87 Alive 38.684210526 IV No 1.87 Alive 21.315789474 IV Unknown 1.87 Alive 24.375 IV No 1.87 Alive 14.934210526 IV Yes 1.87 Alive 10.296052632 III No 1.86 Alive 56.414473684 IV No 1.86 Alive 30.230263158 IV Unknown 1.85 Alive 31.348684211 IV No 1.85 Alive 5.4605263158 IV Yes 1.85 Alive 7.4671052632 IV No 1.85 Alive 0.6907894737 III Yes 1.84 Alive 42.368421053 IV Unknown 1.84 Alive 2.5328947368 IV No 1.83 Alive 27.664473684 III Yes 1.83 Alive 33.980263158 III No 1.82 Alive 20.197368421 III Yes 1.82 Alive 24.013157895 IV Unknown 1.81 Alive 29.078947368 III No 1.8 Dead 11.677631579 III No 1.8 Alive 76.907894737 III Yes 1.8 Alive 66.710526316 IV Unknown 1.8 Dead 37.269736842 III No 1.8 Alive 25.361842105 IV Yes 1.8 Alive 47.664473684 III No 1.8 Alive 45.789473684 IV Yes 1.8 Dead 6.8421052632 IV Unknown 1.8 Alive 41.414473684 IV Yes 1.8 Dead 24.572368421 III No 1.8 Alive 24.309210526 IV Unknown 1.8 Alive 22.730263158 III No 1.8 Alive 15.427631579 IV Unknown 1.8 Alive 9.8684210526 III Yes 1.8 Alive 52.796052632 IV Yes 1.79 Dead 37.796052632 III Yes 1.79 Alive 18.421052632 IV No 1.78 Alive 58.782894737 IV Yes 1.78 Alive 8.2894736842 IV Yes 1.78 Alive 27.861842105 III Unknown 1.77 Alive 11.677631579 IV No 1.76 Alive 42.006578947 IV No 1.76 Alive 9.1118421053 IV Yes 1.75 Alive 4.4078947368 IV No 1.75 Alive 28.782894737 IV No 1.75 Alive 16.578947368 IV Yes 1.75 Alive 15.855263158 IV No 1.75 Alive 12.236842105 IV Unknown 1.75 Alive 42.368421053 IV Yes 1.75 Alive 4.6381578947 III Yes 1.74 Alive 45.328947368 IV No 1.74 Alive 35.131578947 IV No 1.73 Alive 60.394736842 IV No 1.73 Alive 1.9736842105 IV Yes 1.73 Alive 55.592105263 IV Unknown 1.72 Dead 44.407894737 IV No 1.72 Alive 51.151315789 III No 1.72 Alive 26.019736842 IV No 1.72 Alive 19.901315789 IV No 1.72 Alive 11.282894737 IV No 1.72 Alive 20.032894737 III No 1.71 Alive 45.921052632 III Unknown 1.71 Alive 47.927631579 IV Unknown 1.7 Alive 57.993421053 III Yes 1.7 Alive 76.315789474 III No 1.7 Alive 2.5328947368 IV No 1.7 Alive 54.638157895 IV No 1.7 Alive 33.157894737 III No 1.7 Alive 24.309210526 IV No 1.7 Alive 3.125 IV Yes 1.7 Alive 4.0460526316 IV Unknown 1.7 Alive 38.092105263 III No 1.69 Alive . IV No 1.68 Alive 18.552631579 IV Unknown 1.68 Alive 3.6513157895 IV No 1.68 Alive 15.098684211 IV No 1.68 Alive 1.4144736842 IV Yes 1.67 Alive 7.3355263158 III No 1.67 Alive 14.605263158 III Yes 1.66 Alive 0.4276315789 IV No 1.65 Alive 28.684210526 III No 1.65 Alive 35.098684211 IV No 1.65 Alive . IV No 1.65 Alive 25.361842105 IV No 1.65 Alive 31.842105263 IV Unknown 1.65 Alive 4.9671052632 IV No 1.62 Dead 29.638157895 IV Unknown 1.62 Alive 19.111842105 IV No 1.62 Alive 57.631578947 IV Unknown 1.62 Alive 11.118421053 III No 1.6 Alive 67.302631579 IV No 1.6 Alive 58.848684211 IV No 1.6 Alive 39.967105263 IV Yes 1.6 Alive 40.197368421 UNK No 1.6 Alive 29.440789474 III Unknown 1.6 Alive 45 IV Yes 1.6 Alive 13.684210526 IV Unknown 1.6 Alive 16.578947368 III Yes 1.6 Alive 18.190789474 IV Yes 1.6 Alive 27.171052632 III Yes 1.6 Alive 26.875 IV Yes 1.6 Alive 10 IV Yes 1.6 Alive 2.9934210526 IV Yes 1.6 Dead 10.098684211 III Unknown 1.6 Alive 16.118421053 III Yes 1.6 Alive 0.4605263158 III No 1.6 Alive 71.151315789 IV No 1.59 Alive 11.315789474 III No 1.58 Alive 55.394736842 III No 1.58 Alive 0.5263157895 III No 1.58 Alive 29.901315789 IV No 1.57 Alive 30.032894737 III No 1.56 Alive 36.414473684 IV Unknown 1.56 Alive 30.657894737 III Unknown 1.56 Alive 64.802631579 III No 1.56 Alive 13.651315789 IV No 1.56 Alive 43.125 III Yes 1.55 Alive 16.940789474 III No 1.55 Alive 68.322368421 IV No 1.55 Alive 24.901315789 IV No 1.55 Alive 3.4210526316 IV No 1.54 Alive 36.414473684 IV No 1.54 Alive 1.8421052632 IV No 1.53 Dead 28.651315789 IV No 1.52 Alive 37.401315789 UNK No 1.52 Alive 16.677631579 IV No 1.51 Alive 38.223684211 IV No 1.51 Alive 4.9342105263 IV No 1.5 Alive -2.894736842 IV Unknown 1.5 Alive 45.592105263 II Unknown 1.5 Dead 68.848684211 III Yes 1.5 Alive 10.328947368 UNK Yes 1.5 Alive 3.8815789474 IV No 1.5 Alive 12.796052632 IV No 1.5 Alive 6.2171052632 IV No 1.5 Alive 10.098684211 III No 1.5 Alive 53.848684211 IV Unknown 1.5 Alive 36.973684211 IV Unknown 1.5 Alive 20.986842105 IV Unknown 1.5 Alive 21.480263158 III Yes 1.5 Alive 17.631578947 III Unknown 1.5 Alive 75.460526316 IV Yes 1.5 Alive 18.914473684 IV No 1.5 Alive 15.690789474 IV No 1.5 Alive 13.453947368 IV No 1.5 Alive 3.1578947368 IV Yes 1.5 Alive 9.9671052632 IV Unknown 1.5 Alive 26.282894737 IV No 1.5 Alive 13.026315789 III Unknown 1.5 Alive 7.6644736842 IV No 1.5 Alive 0.7894736842 IV No 1.5 Alive 7.9934210526 III Yes 1.5 Alive 10.789473684 III No 1.5 Alive 4.5723684211 IV Unknown 1.5 Alive 19.177631579 IV No 1.5 Alive 44.868421053 IV No 1.49 Alive 5.3618421053 IV No 1.48 Alive 78.519736842 IV Unknown 1.48 Alive 30.559210526 III Unknown 1.47 Alive 5.5921052632 IV Unknown 1.46 Alive 41.710526316 IV No 1.46 Alive 12.138157895 IV No 1.45 Alive 73.256578947 III No 1.45 Alive 31.151315789 III No 1.45 Alive 60.986842105 IV No 1.45 Alive 4.4407894737 IV Unknown 1.45 Alive 35.296052632 IV No 1.45 Alive 25.164473684 IV No 1.45 Alive 4.2105263158 III Unknown 1.45 Alive 2.0065789474 IV No 1.44 Alive 4.0131578947 III No 1.43 Alive 26.282894737 IV Yes 1.43 Alive 48.092105263 IV Yes 1.43 Alive 11.019736842 IV No 1.42 Alive 1.7763157895 IV No 1.42 Alive 9.375 IV Unknown 1.4 Alive 6.7105263158 III No 1.4 Alive 80.197368421 IV No 1.4 Alive 30.361842105 IV No 1.4 Alive 57.401315789 III No 1.4 Alive 84.967105263 III No 1.4 Alive 48.585526316 IV No 1.4 Alive 1.6118421053 III No 1.4 Alive 14.967105263 III Yes 1.4 Alive 9.0131578947 III Unknown 1.4 Alive 18.848684211 IV No 1.4 Alive 0.7565789474 III No 1.4 Alive 18.421052632 V Unknown 1.4 Dead 7.4342105263 IV Unknown 1.4 Alive 7.4013157895 IV Unknown 1.4 Alive 3.7171052632 IV Unknown 1.4 Alive 1.6118421053 IV Yes 1.39 Alive 14.078947368 III Yes 1.38 Alive 1.6776315789 IV Unknown 1.38 Alive 10.394736842 IV No 1.37 Alive 61.875 III Yes 1.37 Alive 19.835526316 IV No 1.36 Alive 0.3947368421 III No 1.35 Alive 53.026315789 IV No 1.35 Alive 0.3947368421 III No 1.35 Alive 2.3684210526 III Unknown 1.35 Alive 23.157894737 III No 1.35 Alive 10.493421053 III No 1.35 Alive 4.6710526316 IV No 1.35 Dead 83.552631579 IV No 1.34 Alive 50.921052632 IV No 1.33 Alive 72.368421053 III No 1.33 Alive 33.815789474 IV No 1.32 Alive 41.611842105 IV Unknown 1.32 Alive 5.5921052632 III Unknown 1.32 Alive 56.282894737 III No 1.32 Alive 84.111842105 IV No 1.32 Alive 36.513157895 IV Unknown 1.31 Alive 39.605263158 IV No 1.31 Alive 49.703947368 IV No 1.3 Alive 79.144736842 III No 1.3 Alive 13.519736842 III No 1.3 Alive 71.151315789 III No 1.3 Alive 45.723684211 III No 1.3 Alive 44.473684211 IV Unknown 1.3 Dead 21.019736842 III Unknown 1.3 Alive 0.7236842105 II Yes 1.3 Alive 47.861842105 IV Unknown 1.3 Alive 1.1842105263 IV No 1.3 Alive 13.355263158 IV No 1.3 Alive 11.842105263 III No 1.3 Alive 12.664473684 IV Yes 1.3 Alive 9.7368421053 IV No 1.3 Alive 9.2434210526 III No 1.3 Alive 9.9671052632 IV Yes 1.3 Alive 2.4342105263 III Unknown 1.29 Alive 12.960526316 III Yes 1.28 Alive 20.328947368 IV No 1.28 Alive 48.815789474 IV No 1.28 Alive 33.388157895 III No 1.28 Alive 1.7105263158 IV Yes 1.27 Dead 50.822368421 IV No 1.27 Dead 14.276315789 III No 1.27 Alive 15.394736842 III Unknown 1.26 Alive 5.5592105263 IV No 1.25 Alive 13.125 IV No 1.25 Alive 9.6052631579 IV No 1.25 Alive 29.309210526 III Unknown 1.25 Alive -8.453947368 III No 1.25 Alive 32.861842105 III No 1.25 Alive 87.5 IV Unknown 1.25 Alive 3.5197368421 IV Unknown 1.25 Alive 22.171052632 III No 1.24 Alive 29.671052632 UNK Unknown 1.23 Dead 86.677631579 IV Unknown 1.23 Alive 35.888157895 IV No 1.22 Alive 34.046052632 III No 1.22 Alive 8.5197368421 IV Unknown 1.21 Alive 29.506578947 UNK Unknown 1.2 Alive 0.5592105263 III No 1.2 Alive 88.421052632 III Yes 1.2 Alive 79.769736842 IV No 1.2 Alive 9.5394736842 UNK No 1.2 Alive 77.828947368 IV No 1.2 Alive 0.625 III Yes 1.2 Alive 80.625 III No 1.2 Alive 59.802631579 III No 1.2 Alive 70.822368421 III No 1.2 Alive 67.467105263 III No 1.2 Alive 58.75 III No 1.2 Alive 28.125 II No 1.2 Alive 47.565789474 IV Unknown 1.2 Alive 61.25 IV No 1.2 Alive 29.539473684 III Unknown 1.2 Alive 49.013157895 III No 1.2 Alive 12.006578947 IV No 1.2 Alive 21.513157895 IV Unknown 1.2 Alive 13.815789474 IV Unknown 1.2 Alive 0.5263157895 III No 1.2 Alive 1.6118421053 IV No 1.2 Alive 9.2434210526 III No 1.2 Alive 1.7105263158 II Unknown 1.2 Alive 25.098684211 IV Unknown 1.2 Alive 37.006578947 III Yes 1.2 Alive 47.697368421 IV No 1.2 Alive 9.4078947368 III No 1.18 Alive 13.75 III No 1.18 Alive 12.5 III No 1.17 Alive 54.375 IV No 1.15 Alive 59.144736842 III Yes 1.15 Alive . III Unknown 1.15 Alive 37.039473684 III Unknown 1.15 Alive 1.0197368421 IV Unknown 1.15 Alive 0.8552631579 III No 1.15 Alive 34.506578947 IV No 1.15 Alive 24.835526316 IV No 1.15 Alive 16.414473684 III No 1.15 Alive . IV Unknown 1.15 Alive 5.1644736842 II No 1.14 Alive 30.197368421 IV Yes 1.14 Alive 8.2894736842 IV No 1.12 Alive 50.230263158 III No 1.12 Alive 13.552631579 IV No 1.1 Alive 93.519736842 III No 1.1 Alive 88.618421053 III No 1.1 Alive 60.789473684 III Unknown 1.1 Alive . III Yes 1.1 Alive 55 III No 1.1 Alive 34.210526316 IV Yes 1.1 Alive 0.5592105263 III No 1.1 Alive 39.111842105 III No 1.1 Alive 21.743421053 IV No 1.1 Alive 18.190789474 III Unknown 1.1 Alive 41.315789474 III No 1.1 Alive 11.118421053 IV No 1.1 Alive 32.236842105 III No 1.1 Alive 31.546052632 III No 1.1 Alive 29.506578947 IV Unknown 1.1 Alive 28.486842105 IV No 1.1 Alive 29.736842105 III Unknown 1.1 Alive 20.394736842 III Unknown 1.1 Alive 29.243421053 IV Unknown 1.1 Dead 80.921052632 IV No 1.1 Alive 15.789473684 IV No 1.1 Alive 15.032894737 III No 1.1 Alive 5.0657894737 III Yes 1.1 Alive 7.0394736842 IV No 1.1 Alive 34.967105263 III Yes 1.1 Alive 7.1052631579 III No 1.1 Alive 5.5592105263 II Yes 1.1 Alive 15.197368421 IV Unknown 1.1 Alive 4.9013157895 IV Yes 1.1 Alive 4.375 IV Unknown 1.1 Alive 10.065789474 IV Unknown 1.08 Alive 6.7763157895 IV Unknown 1.08 Alive 15.427631579 IV No 1.07 Alive 13.125 III Yes 1.05 Alive 8.5197368421 II Unknown 1.05 Alive 30.427631579 IV Yes 1.05 Alive 1.1513157895 III No 1.05 Alive 4.5065789474 III No 1.05 Alive 6.6776315789 IV No 1.05 Alive 3.2894736842 IV No 1.05 Alive 4.6052631579 III Unknown 1.05 Alive 1.7105263158 III Unknown 1.04 Alive 42.335526316 III Unknown 1.04 Alive 12.894736842 III Unknown 1.04 Alive 4.6710526316 III Unknown 1.04 Alive 19.177631579 III No 1.04 Alive 15.394736842 IV Yes 1.04 Alive 13.289473684 III Yes 1.04 Alive 8.5197368421 III No 1.04 Alive 9.2105263158 III No 1.03 Alive 19.111842105 III No 1.02 Alive 5.2631578947 III No 1.02 Alive 44.046052632 IV No 1.02 Alive 34.375 III No 1.01 Alive 11.381578947 III No 1.01 Alive 79.934210526 III Unknown 1 Alive 36.513157895 III No 1 Dead 14.078947368 III No 1 Alive 13.322368421 III No 1 Alive 6.1513157895 III No 1 Alive 57.828947368 III Unknown 1 Alive 42.138157895 IV Yes 1 Alive 23.092105263 III No 1 Alive 9.1447368421 III Unknown 1 Alive 1.3815789474 III No 1 Alive 12.664473684 IV Unknown 1 Alive 10.559210526 III Unknown 1 Alive 2.2039473684 III Unknown 1 Alive 16.019736842 III Unknown 1 Alive 13.026315789 III No 1 Alive 15.921052632 IV Unknown 1 Alive 23.223684211 IV No 1 Alive 17.072368421 IV Yes 1 Alive 17.072368421 IV Yes 1 Alive 15.427631579 IV No 1 Alive 9.9671052632 III No 1 Alive 22.368421053 III No 1 Alive 12.171052632 III Unknown 1 Alive 67.072368421 IV Yes 1 Alive 2.2697368421 III No 1 Alive 1.1513157895 III No 1 Alive 1.8092105263 III No 1 Alive 63.782894737 II No 1 Alive 51.546052632 III No 1 Alive 50.361842105 IV Unknown 1 Alive 9.5065789474 IV No 1 Alive 9.2434210526 IV No 0.99 Alive 14.572368421 IV No 0.98 Alive 13.190789474 III No 0.98 Alive 53.651315789 III No 0.98 Alive 2.6315789474 III No 0.97 Alive 23.914473684 III No 0.97 Alive 60 III No 0.97 Alive 38.914473684 III No 0.96 Alive 39.111842105 III No 0.95 Alive 40.723684211 III No 0.95 Alive 13.914473684 IV No 0.95 Alive 74.440789474 III Unknown 0.95 Alive 4.9013157895 III No 0.95 Alive 3.75 II No 0.95 Alive 5.3289473684 IV No 0.95 Alive 7.2697368421 IV No 0.95 Alive 8.3881578947 IV No 0.95 Alive 61.085526316 III Unknown 0.94 Alive 36.217105263 I Unknown 0.94 Alive . III No 0.94 Alive 11.578947368 III Unknown 0.94 Alive 64.736842105 III No 0.93 Alive 3.9144736842 III Unknown 0.93 Alive 34.144736842 IV No 0.92 Alive 58.980263158 III Unknown 0.92 Alive 11.315789474 III No 0.92 Alive 11.644736842 IV No 0.92 Alive 55.789473684 III Unknown 0.92 Alive 29.506578947 III No 0.92 Alive 22.072368421 III No 0.92 Alive 4.3421052632 IV Unknown 0.92 Alive 4.2105263158 III No 0.91 Alive 0.5263157895 III Unknown 0.9 Dead 18.519736842 III Unknown 0.9 Alive 7.5986842105 III Unknown 0.9 Alive 0.625 III No 0.9 Alive 25.263157895 V Unknown 0.9 Dead 8.0592105263 IV No 0.9 Alive 36.940789474 III No 0.9 Alive 37.401315789 IV No 0.9 Alive 36.414473684 IV Unknown 0.9 Alive 9.9342105263 III Unknown 0.9 Alive 51.184210526 IV Unknown 0.9 Alive 0.9539473684 IV Unknown 0.9 Alive 30.625 III Unknown 0.9 Alive 27.072368421 II Unknown 0.9 Alive . III Unknown 0.9 Alive 54.835526316 III No 0.9 Alive 13.947368421 IV No 0.9 Alive 16.743421053 III No 0.9 Alive 9.5394736842 III Unknown 0.9 Alive 18.651315789 III Unknown 0.9 Alive 4.8355263158 IV No 0.9 Alive 7.9605263158 IV Yes 0.9 Alive 81.25 III Unknown 0.9 Alive 14.967105263 III No 0.9 Alive 2.1381578947 IV No 0.89 Alive 47.434210526 III No 0.89 Alive 11.973684211 III Yes 0.88 Alive 24.243421053 IV No 0.88 Alive 22.368421053 IV No 0.87 Alive 25.427631579 III No 0.86 Alive 7.6315789474 III No 0.86 Alive 33.453947368 III No 0.86 Alive 30.296052632 II No 0.85 Alive 72.796052632 IV No 0.85 Alive 0.9868421053 III No 0.85 Alive 36.118421053 III Unknown 0.85 Alive 16.677631579 III No 0.85 Alive 2.0065789474 II Unknown 0.85 Alive 5.9868421053 III Unknown 0.85 Alive . III Unknown 0.85 Alive 19.802631579 II No 0.85 Alive 17.532894737 IV No 0.85 Alive 2.7302631579 III Yes 0.85 Alive 14.407894737 IV Yes 0.85 Alive 0.9539473684 III No 0.85 Alive 11.940789474 III No 0.84 Alive 23.486842105 III No 0.83 Alive 35.526315789 III No 0.83 Alive 16.052631579 III No 0.82 Alive 14.539473684 III No 0.82 Alive 0.625 III Unknown 0.82 Alive 0.1973684211 IV Unknown 0.82 Alive 29.835526316 IV Unknown 0.82 Alive 13.486842105 III No 0.82 Alive 5.7236842105 III No 0.82 Alive 5.0986842105 III Unknown 0.81 Alive 93.717105263 II No 0.81 Alive 0.5592105263 III No 0.81 Alive 24.703947368 III Unknown 0.8 Alive 0.2302631579 III No 0.8 Alive 62.269736842 III No 0.8 Alive 77.861842105 III Yes 0.8 Alive 16.151315789 III Yes 0.8 Alive 44.802631579 III No 0.8 Alive 34.013157895 IV No 0.8 Alive 25.098684211 III Unknown 0.8 Alive 31.480263158 III Unknown 0.8 Alive 37.105263158 III No 0.8 Alive 0.8881578947 II No 0.8 Alive 30.921052632 III No 0.8 Alive 8.9473684211 II Unknown 0.8 Alive 31.414473684 III No 0.8 Alive 16.019736842 IV Unknown 0.8 Alive 23.881578947 III Unknown 0.8 Alive 1.875 II No 0.8 Alive 97.796052632 III Yes 0.8 Alive 8.8157894737 IV Unknown 0.8 Alive 7.8947368421 III Unknown 0.8 Alive 7.1052631579 III Yes 0.8 Alive 7.2039473684 II No 0.8 Alive 0.3618421053 III No 0.8 Alive 2.0394736842 III No 0.79 Alive 38.618421053 I No 0.79 Alive 7.1052631579 III No 0.79 Alive 59.111842105 III No 0.78 Alive 50.230263158 II No 0.78 Alive 7.1381578947 III No 0.77 Alive 39.506578947 III Unknown 0.77 Alive 26.118421053 III Unknown 0.77 Alive 12.796052632 IV No 0.76 Alive 24.210526316 III No 0.76 Alive 30.065789474 III No 0.76 Alive 29.111842105 III No 0.76 Alive 25.822368421 III No 0.76 Alive 6.4473684211 II No 0.75 Alive 39.769736842 III Unknown 0.75 Alive . II Unknown 0.75 Alive 1.875 II No 0.75 Alive 65.625 III No 0.75 Alive 7.1710526316 IV Yes 0.75 Alive 32.532894737 II No 0.75 Alive 3.3881578947 UNK Unknown 0.75 Dead 11.809210526 III No 0.75 Alive 80.296052632 IV No 0.75 Dead 40.625 III No 0.75 Alive 1.9736842105 III Unknown 0.75 Alive 15.855263158 IV No 0.75 Alive 0.7565789474 III Unknown 0.75 Dead 31.414473684 III No 0.75 Alive 2.2039473684 III No 0.75 Alive 26.677631579 III No 0.75 Alive 41.085526316 III Unknown 0.75 Alive 50.822368421 II Unknown 0.74 Alive 9.4078947368 III No 0.74 Alive 61.085526316 II No 0.74 Alive 2.1052631579 II No 0.74 Alive 7.3684210526 II No 0.73 Alive 1.875 III No 0.72 Alive 89.868421053 IV Yes 0.72 Alive 4.1447368421 III No 0.72 Alive 55.328947368 III No 0.72 Alive 31.578947368 II No 0.72 Alive 0.2960526316 IV Unknown 0.72 Alive 0.4605263158 III No 0.71 Alive 31.414473684 II Unknown 0.71 Alive 90.164473684 IV No 0.7 Alive 5.0657894737 III Unknown 0.7 Alive 0.8223684211 IV No 0.7 Alive 34.638157895 II No 0.7 Dead 2.9934210526 II No 0.7 Alive 1.5131578947 III Unknown 0.7 Alive 0.9210526316 III No 0.7 Alive 12.532894737 II Yes 0.7 Alive . II No 0.7 Alive 3.9473684211 II No 0.7 Alive 7.9276315789 III Yes 0.7 Alive 19.901315789 II No 0.7 Alive 1.5789473684 III Yes 0.7 Alive 30.888157895 III No 0.7 Alive 13.355263158 III No 0.7 Alive 5.2960526316 III Yes 0.69 Alive 23.618421053 III Unknown 0.69 Alive 34.605263158 II Yes 0.68 Dead 25.625 IV No 0.68 Alive 57.631578947 III No 0.68 Alive 40.460526316 II No 0.68 Alive 37.335526316 II No 0.67 Alive 55.559210526 III No 0.67 Alive 0.2960526316 III Unknown 0.67 Alive 58.848684211 I No 0.66 Alive 22.598684211 III No 0.65 Alive 23.980263158 III No 0.65 Alive 27.434210526 III No 0.65 Alive 43.355263158 II No 0.65 Alive 20.855263158 III No 0.65 Alive 52.269736842 III No 0.65 Alive 23.355263158 III No 0.65 Alive 36.052631579 III No 0.65 Alive . III Yes 0.65 Alive 12.236842105 III Unknown 0.65 Alive 1.8421052632 II No 0.65 Alive 19.144736842 II Unknown 0.65 Alive . II Unknown 0.65 Alive 11.875 III No 0.65 Alive 7.5986842105 II No 0.65 Alive 1.2171052632 III No 0.65 Alive 32.894736842 II No 0.64 Alive 18.223684211 UNK No 0.64 Alive 0.4276315789 III No 0.64 Alive 11.677631579 III No 0.63 Alive . II No 0.63 Alive 3.125 III Unknown 0.63 Alive 9.8684210526 II No 0.63 Alive 37.993421053 II No 0.63 Alive . II Unknown 0.63 Alive 8.7171052632 IV No 0.63 Alive 46.973684211 III No 0.62 Alive 73.881578947 III No 0.62 Alive 1.2828947368 III No 0.62 Alive 2.9934210526 III No 0.62 Alive 34.539473684 III Unknown 0.62 Alive 35.625 II No 0.62 Alive 39.177631579 III No 0.62 Alive . II Unknown 0.61 Alive 0.5592105263 II Unknown 0.61 Alive 3.0921052632 II No 0.61 Alive 7.0065789474 II Unknown 0.6 Alive 60.197368421 II No 0.6 Alive 54.078947368 III No 0.6 Alive 1.1184210526 III No 0.6 Alive 14.769736842 III No 0.6 Alive 12.236842105 II No 0.6 Alive 56.315789474 II Unknown 0.6 Alive 2.0723684211 II No 0.6 Alive 31.052631579 II No 0.6 Alive 57.763157895 III Unknown 0.6 Alive 30.888157895 II No 0.6 Alive 34.736842105 II No 0.6 Alive 28.355263158 II No 0.6 Alive . III Unknown 0.6 Alive . II Unknown 0.6 Alive 0.3618421053 III No 0.6 Alive . III No 0.6 Alive 9.6052631579 III No 0.6 Alive 11.546052632 III No 0.6 Alive 0.4605263158 III No 0.6 Alive 46.743421053 III No 0.6 Alive 3.9473684211 II No 0.6 Alive 32.894736842 III No 0.6 Alive 1.875 II No 0.6 Alive . II No 0.59 Alive . III No 0.59 Alive 8.0592105263 III Yes 0.59 Alive 7.5657894737 II No 0.58 Alive . III Unknown 0.58 Alive 32.861842105 III No 0.58 Alive 11.644736842 III Unknown 0.57 Alive 4.7697368421 II No 0.57 Dead 14.177631579 III Yes 0.57 Alive 17.861842105 III No 0.57 Alive 10.032894737 II No 0.55 Alive 2.4342105263 II No 0.55 Alive 46.611842105 III No 0.55 Alive 63.815789474 III No 0.55 Alive 72.236842105 III Unknown 0.55 Alive 70.230263158 II No 0.55 Alive 0.4605263158 III No 0.55 Alive 37.796052632 III No 0.55 Alive 0.4605263158 III No 0.55 Dead 4.7368421053 II Unknown 0.55 Alive 19.605263158 II Unknown 0.55 Alive . II No 0.55 Alive 30.592105263 II Unknown 0.55 Alive 0 III Unknown 0.55 Alive 0.7894736842 II Unknown 0.54 Alive 24.177631579 III No 0.54 Alive 19.901315789 II Unknown 0.54 Alive 48.75 II Unknown 0.54 Alive 1.1842105263 III Unknown 0.54 Alive 9.8026315789 II No 0.53 Alive 4.6381578947 II No 0.53 Alive 12.598684211 II Unknown 0.52 Alive . III No 0.52 Alive 0.3618421053 II No 0.52 Alive 34.703947368 II Unknown 0.52 Alive 8.0592105263 II No 0.52 Alive . III No 0.51 Alive 44.177631579 II No 0.51 Alive 42.138157895 III No 0.51 Alive 2.8289473684 III Unknown 0.51 Alive 16.875 II No 0.51 Alive 14.309210526 II No 0.51 Alive . III Unknown 0.51 Alive 6.4144736842 III No 0.5 Alive 63.256578947 II No 0.5 Alive 4.6381578947 II No 0.5 Alive . II No 0.5 Alive 15.065789474 III No 0.5 Alive 57.105263158 III No 0.5 Alive 38.026315789 II No 0.5 Alive 0.8881578947 II No 0.5 Alive 0.5592105263 II No 0.5 Alive . III Unknown 0.5 Alive 39.078947368 II No 0.5 Alive . II No 0.5 Alive 7.0723684211 II Unknown 0.5 Alive 0.8552631579 II No 0.5 Alive 15.263157895 II No 0.5 Alive . III Unknown 0.5 Alive 23.256578947 III No 0.5 Alive 1.2828947368 III Unknown 0.5 Alive 19.078947368 III No 0.5 Alive 3.0263157895 II Unknown 0.5 Alive 25.526315789 II Yes 0.5 Alive 15.888157895 II Yes 0.5 Alive 22.467105263 II No 0.5 Alive 61.151315789 II Yes 0.5 Alive 12.467105263 II No 0.5 Alive 5.9868421053 III No 0.5 Alive 10.986842105 IV Unknown 0.5 Alive 4.0460526316 II No 0.5 Alive 1.1842105263 II No 0.49 Alive 15.460526316 II No 0.49 Alive 17.006578947 II Unknown 0.48 Alive 74.407894737 III No 0.48 Alive 40.328947368 II No 0.48 Alive 35.657894737 III Unknown 0.48 Alive 25.065789474 II No 0.48 Alive 19.638157895 II No 0.47 Alive 3.8815789474 III No 0.47 Alive 7.1381578947 II No 0.47 Alive 12.467105263 I No 0.47 Alive 30.625 II No 0.46 Alive 1.0526315789 II No 0.46 Alive 1.1513157895 II No 0.46 Alive 10.197368421 II No 0.46 Alive 0.6578947368 II No 0.45 Alive . III No 0.45 Alive . II No 0.45 Alive 83.355263158 II No 0.45 Alive 0.2631578947 II No 0.45 Alive 76.677631579 II No 0.45 Alive 46.282894737 II No 0.45 Alive 43.059210526 III No 0.45 Alive 46.677631579 II No 0.45 Alive 2.3355263158 II Unknown 0.45 Alive 1.2828947368 III No 0.45 Alive 25.822368421 II No 0.45 Alive 3.2565789474 II Unknown 0.45 Alive 9.2105263158 II No 0.45 Alive 9.8684210526 III Unknown 0.45 Alive . II No 0.45 Alive 8.6842105263 II Unknown 0.44 Alive 0.625 II No 0.43 Alive 0.8223684211 II No 0.43 Alive 1.7763157895 III No 0.43 Alive 13.684210526 II Unknown 0.42 Alive . II No 0.42 Alive 33.585526316 II Unknown 0.42 Alive 1.3815789474 II No 0.42 Alive 12.697368421 II Unknown 0.42 Alive 12.763157895 III No 0.41 Alive 88.717105263 II No 0.41 Alive 7.7631578947 II No 0.41 Alive 83.388157895 II No 0.41 Alive 6.9078947368 II Unknown 0.41 Alive 35.394736842 II No 0.4 Alive . III Unknown 0.4 Alive 51.611842105 III Unknown 0.4 Alive 38.717105263 II No 0.4 Alive 3.7171052632 III No 0.4 Alive 32.960526316 II No 0.4 Alive . II No 0.4 Alive . III No 0.4 Alive 38.717105263 II No 0.4 Alive 16.348684211 II No 0.4 Alive . II Unknown 0.4 Alive 36.480263158 II No 0.4 Alive 24.210526316 III No 0.4 Alive 7.1052631579 III No 0.4 Alive 40.230263158 II No 0.4 Alive 0.625 II Unknown 0.4 Alive . II No 0.4 Alive 2.7960526316 III Unknown 0.4 Alive 52.5 II No 0.4 Alive 35.888157895 II No 0.4 Alive 3.0263157895 UNK No 0.4 Alive 60.822368421 II No 0.4 Alive 64.473684211 II Unknown 0.4 Alive 2.5986842105 II Unknown 0.4 Alive 80.822368421 II No 0.4 Alive 16.875 II Unknown 0.4 Alive . II No 0.4 Alive 29.934210526 II No 0.4 Alive . II Unknown 0.4 Alive 12.763157895 II Yes 0.4 Alive 11.381578947 II No 0.4 Alive 13.256578947 II No 0.4 Alive -3.059210526 II No 0.4 Alive . II No 0.4 Alive 12.861842105 II No 0.4 Alive 5.2960526316 II No 0.4 Alive 7.8289473684 II No 0.4 Alive 4.3421052632 II Unknown 0.4 Alive 1.1513157895 II No 0.4 Alive 6.7105263158 II No 0.4 Alive 7.4013157895 II No 0.4 Alive . III No 0.4 Alive 3.8157894737 II Unknown 0.39 Alive 50.657894737 II Unknown 0.38 Alive 37.072368421 II Unknown 0.38 Alive 27.565789474 II Unknown 0.38 Alive . II Unknown 0.38 Alive . II Unknown 0.38 Alive 13.947368421 II Unknown 0.38 Alive 51.25 II No 0.37 Alive 9.0789473684 UNK Unknown 0.36 Alive 0.8552631579 II No 0.36 Alive 14.638157895 II No 0.36 Alive 89.835526316 II No 0.36 Alive 3.5197368421 II No 0.36 Alive 5.7236842105 II No 0.36 Alive 8.5855263158 II No 0.36 Alive . II No 0.35 Alive . II No 0.35 Alive 36.842105263 II No 0.35 Alive 10.098684211 II No 0.35 Alive 62.532894737 II No 0.35 Alive 17.203947368 II No 0.35 Alive 49.539473684 II No 0.35 Alive 3.9144736842 III Unknown 0.35 Alive . II No 0.35 Alive 0.1315789474 II Unknown 0.35 Alive 8.0921052632 II Unknown 0.35 Alive 47.631578947 II Unknown 0.35 Alive 18.914473684 II No 0.35 Alive 25.888157895 II Unknown 0.35 Alive 4.1118421053 II Yes 0.35 Alive 42.993421053 III No 0.35 Alive 11.907894737 II No 0.35 Alive 0.4934210526 III No 0.35 Alive 0.1973684211 II No 0.34 Alive 5.4934210526 II No 0.34 Alive . II No 0.34 Alive 6.9078947368 II No 0.34 Alive 9.9342105263 II No 0.33 Alive 36.085526316 II No 0.33 Alive 77.894736842 II No 0.33 Alive . II No 0.33 Alive 96.710526316 III No 0.33 Alive 11.282894737 II Unknown 0.33 Alive 19.473684211 II No 0.33 Alive 37.993421053 II Unknown 0.33 Alive . II No 0.32 Alive 18.848684211 II Unknown 0.32 Alive 3.75 II Unknown 0.32 Alive . II No 0.32 Alive 25.361842105 II No 0.32 Alive 0.8223684211 II No 0.32 Alive 1.1842105263 III No 0.32 Alive 4.0460526316 II No 0.32 Alive 2.7302631579 II No 0.31 Alive 58.453947368 II Unknown 0.31 Alive 16.151315789 II No 0.31 Alive 37.105263158 UNK Unknown 0.31 Alive 38.881578947 III Unknown 0.31 Alive . II No 0.3 Alive 4.6710526316 II Unknown 0.3 Alive 71.776315789 II Unknown 0.3 Alive 11.019736842 II No 0.3 Alive 31.611842105 II Unknown 0.3 Alive 2.2039473684 II No 0.3 Alive 0.4934210526 II No 0.3 Alive 22.368421053 II Yes 0.3 Alive 49.375 II Unknown 0.3 Alive 6.8092105263 II Unknown 0.3 Alive 17.730263158 II No 0.3 Alive . II Unknown 0.3 Alive 6.6118421053 II No 0.3 Alive 3.7828947368 II No 0.3 Alive . II No 0.3 Alive 31.644736842 II Unknown 0.3 Alive . I No 0.3 Alive . III No 0.3 Alive . II Yes 0.3 Alive 0.4605263158 II No 0.3 Alive . I Unknown 0.3 Alive 5.0328947368 II Yes 0.3 Alive 26.315789474 III No 0.3 Alive . II No 0.3 Alive 0.4934210526 II No 0.3 Alive 14.144736842 II No 0.3 Alive 6.4144736842 II No 0.3 Alive 6.7763157895 II Unknown 0.3 Alive 11.743421053 II Unknown 0.3 Alive 25.263157895 II Unknown 0.29 Alive . II Unknown 0.29 Alive . II No 0.28 Alive 84.703947368 II No 0.28 Alive 78.651315789 II Unknown 0.28 Alive . II Unknown 0.28 Alive 2.9934210526 II No 0.28 Dead 7.2697368421 II No 0.27 Alive 5.1644736842 II No 0.27 Alive 28.519736842 II Unknown 0.27 Alive . II Unknown 0.27 Alive . II No 0.27 Alive . II No 0.26 Alive 10.789473684 II Unknown 0.26 Alive 0.4605263158 II Unknown 0.26 Alive 36.151315789 II No 0.26 Alive 18.782894737 II No 0.26 Alive 71.282894737 II No 0.25 Alive 0.3289473684 II Unknown 0.25 Alive 0.8881578947 II No 0.25 Alive 0.2960526316 II No 0.25 Alive . II Unknown 0.25 Alive 7.8289473684 II Unknown 0.25 Alive 1.5131578947 II Unknown 0.25 Alive 24.638157895 II No 0.25 Alive 4.9671052632 II No 0.24 Alive 33.75 II No 0.24 Alive 31.315789474 II Unknown 0.24 Alive 84.671052632 II No 0.24 Alive 6.6776315789 II No 0.23 Alive 1.3157894737 II No 0.23 Alive 4.2105263158 II No 0.23 Alive 17.960526316 II No 0.23 Alive 2.2039473684 II No 0.22 Alive 23.322368421 II No 0.2 Alive 89.868421053 II No 0.2 Alive 58.355263158 I Unknown 0.2 Alive 26.677631579 II No 0.2 Alive 2.3026315789 II No 0.2 Alive . II No 0.2 Alive 3.9473684211 II No 0.2 Alive 6.9078947368 II No 0.2 Alive 4.9013157895 II No 0.2 Alive 11.907894737 III Unknown 0.19 Alive . II No 0.18 Alive 28.947368421 II No 0.18 Alive 47.631578947 II No 0.18 Alive 0.625 II No 0.13 Alive 1.1842105263 II No 0.1 Alive 6.0197368421 I No 0.1 Dead 8.9144736842 I Unknown 0.1 Alive 65.723684211 II Unknown 0 Alive . I No 0 Alive 47.631578947 I No 0 Alive 0.9868421053 I No 0 Alive . II Unknown 0 Dead 18.388157895 I No 0 Alive . III Unknown 0 Alive . I No 0 Alive 82.763157895 I No 0 Alive 95.592105263 I No 0 Alive 0.4934210526 I No 0 Alive 0.4605263158 I Unknown 0 Alive . I No 0 Alive 23.322368421 I No 0 Alive 73.092105263 I No 0 Alive . UNK No 0 Alive . I Unknown 0 Dead 6.4473684211 I No 0 Alive . I No 0 Alive . V Unknown 0 Dead 16.875 I Unknown 0 Alive . I No 0 Alive 37.072368421 II Unknown 0 Alive 95.164473684 I No 0 Alive 56.019736842 I No 0 Alive 23.322368421 I Unknown 0 Alive 29.276315789 II Unknown 0 Alive 52.368421053 I No 0 Alive 48.355263158 II No 0 Alive 0.5592105263 I No 0 Alive 6.8092105263 I No 0 Alive 1.2171052632 I No 0 Alive 2.0065789474 I No 0 Alive 40.296052632 IV Unknown 0 Alive 0.3947368421 I No 0 Alive . I No 0 Alive . I No 0 Alive . I No 0 Alive 39.309210526 I No 0 Alive 2.4342105263 I Unknown 0 Alive 10.361842105 I No 0 Alive . I No 0 Alive 4.7697368421 I No 0 Alive . IV Unknown 0 Alive 84.835526316 I No 0 Alive 23.256578947 I Unknown 0 Alive 38.125 I Unknown 0 Alive 49.506578947 I Unknown 0 Alive 6.9407894737 I No 0 Alive . I No 0 Alive 43.157894737 I Unknown 0 Alive 10 IV Unknown 0 Alive 1.1513157895 I No 0 Alive 2.7960526316 I Unknown 0 Alive 13.585526316 I Unknown 0 Alive 2.1052631579 I Unknown 0 Alive 23.026315789 I Unknown 0 Alive 36.973684211 IV Unknown 0 Alive . I Unknown 0 Alive . I Unknown 0 Alive . I No 0 Alive . I No 0 Alive 0.4934210526 I No 0 Alive 13.782894737 I Unknown 0 Alive 32.171052632 I Unknown 0 Alive 34.736842105 I No 0 Alive . I No 0 Alive 4.0131578947 I Unknown 0 Alive 0.8881578947 UNK Unknown 0 Alive 33.157894737 V Yes 0 Alive 9.9013157895 I Unknown 0 Alive 2.0723684211 II Unknown 0 Alive 72.039473684 II Unknown 0 Alive 20.230263158 I Unknown 0 Alive 1.3815789474 IV Unknown 0 Alive 88.223684211 I No 0 Alive 2.2697368421 I No 0 Alive 71.25 I No 0 Alive 46.940789474 II Unknown 0 Alive 62.664473684 UNK No 0 Alive 27.598684211 II Unknown 0 Alive 65.559210526 IV Yes 0 Dead 15.526315789 I No 0 Alive 2.7631578947 III Unknown 0 Alive . I Unknown 0 Alive 17.5 IV Unknown 0 Dead 0.8552631579 III Unknown 0 Alive 28.322368421 III No 0 Alive 23.651315789 III Yes 0 Alive 2.6644736842 I Unknown 0 Alive 10.065789474 UNK Unknown 0 Alive 2.1381578947 IV Unknown 0 Alive 0.4934210526 I Unknown 0 Alive 5.2631578947 III Unknown 0 Dead 11.381578947 III Unknown 0 Alive 4.6710526316 IV Unknown 0 Alive 19.605263158 I No 0 Alive 0.4276315789 I Unknown 0 Alive 14.802631579 I Unknown 0 Alive 1.4144736842 IV Unknown 0 Alive 44.605263158 III Unknown 0 Alive 5.1315789474 I Unknown 0 Alive 0.9868421053 UNK Unknown 0 Alive 9.5065789474 I Unknown 0 Alive 0.2302631579 I No 0 Alive 25.625 IV Unknown 0 Alive 74.769736842 I Unknown 0 Alive 53.947368421 I Unknown 0 Alive 15.263157895 I No 0 Alive 14.013157895 III Unknown 0 Alive 70.328947368 III Unknown 0 Alive 1.4473684211 I No 0 Alive 11.381578947 I No 0 Alive 1.4802631579 I No 0 Alive 1.3486842105 UNK Unknown 0 Alive 1.875 I Unknown 0 Alive 3.6513157895 I Yes 0 Alive 9.8026315789 IV Yes 0 Alive 12.039473684 I No 0 Alive 8.5197368421 II No 0 Alive . III Unknown 0 Alive 36.184210526 I Unknown 0 Alive 3.4539473684 III Unknown 0 Alive 31.25 III Unknown 0 Alive 6.7105263158 III Yes 0 Alive 2.8289473684 I No 0 Alive 1.7105263158 I No 0 Alive 0.6907894737 I Unknown 0 Alive . III Unknown 0 Alive 7.4342105263 I Unknown 0 Alive . I No 0 Alive . I No 0 Alive . UNK Unknown 0 Alive 6.7434210526 I Unknown 0 Alive . I No 0 Alive . I No 0 Alive . IV Unknown 0 Alive 2.7302631579 I No 0 Alive 2.2368421053 I No 0 Alive . II Unknown 0 Alive 27.434210526 I Unknown 0 Dead 33.717105263 IV Unknown 0 Alive 19.046052632 IV Unknown 0 Alive . I Unknown 0 Alive 2.0394736842 IV Yes 0 Alive 73.618421053 I Unknown 0 Dead 9.9671052632 I No 0 Alive 21.644736842 III Unknown 0 Alive 10.986842105 ; /**************************************************** * The following data set is mentioned on page 127, * * where it is used to produce output 4.5. * * A slight change was made to the data causing the * * output to differ slightly from that in the book * ****************************************************/ data; input DFSTIME DFSCENS LOCCODE LOC1 LOC2 LOC3 CLARK sex ulcer thickness; datalines; 10.395 1 2 0 1 0 4 1 . . 89.868 0 3 0 0 1 2 1 0 0.20 63.257 0 1 1 0 0 3 1 0 0.50 23.914 0 1 1 0 0 3 0 0 0.97 52.895 0 1 1 0 0 3 0 0 1.35 39.770 0 3 0 0 1 2 1 0 0.75 57.336 0 2 0 1 0 4 1 0 2.91 38.618 0 3 0 0 1 3 1 0 0.79 6.711 0 2 0 1 0 4 0 . 1.40 22.204 1 1 1 0 0 3 1 . 2.50 8.750 1 2 0 1 0 4 1 1 3.00 14.967 0 1 1 0 0 4 0 1 3.23 37.072 0 2 0 1 0 2 1 . 0.38 10.329 0 2 0 1 0 3 1 . 2.80 39.507 0 2 0 1 0 3 0 0 0.77 15.493 1 2 0 1 0 . 0 1 3.80 37.533 1 3 0 0 1 . 1 . 7.00 36.513 0 1 1 0 0 3 1 . 1.00 2.599 0 2 0 1 0 2 1 0 0.90 51.250 0 2 0 1 0 3 1 0 0.65 . 1 4 0 0 0 4 1 . . 4.671 0 2 0 1 0 2 1 0 0.30 13.783 1 3 0 0 1 3 1 1 2.40 8.026 1 1 1 0 0 4 1 0 1.50 22.566 1 2 0 1 0 4 1 1 1.20 23.980 0 2 0 1 0 3 1 0 0.65 24.178 0 1 1 0 0 2 0 . 0.54 1.875 0 1 1 0 0 2 1 . 0.75 77.105 0 2 0 1 0 4 1 . 1.80 28.882 1 2 0 1 0 2 1 0 0.25 101.974 0 1 1 0 0 3 0 0 0.47 24.276 1 4 0 0 0 . 1 0 1.00 74.408 0 1 1 0 0 2 0 . 0.48 85.592 0 1 1 0 0 4 1 0 2.00 42.368 0 2 0 1 0 3 1 1 1.84 96.842 0 1 1 0 0 4 0 . 1.60 151.447 1 3 0 0 1 3 1 1 1.43 114.013 0 2 0 1 0 2 0 . 0.25 93.520 0 2 0 1 0 4 1 0 1.10 83.355 0 2 0 1 0 2 1 0 0.45 -72.434 1 2 0 1 0 3 1 0 2.10 85.329 0 2 0 1 0 4 0 . 3.00 57.993 0 2 0 1 0 4 0 . 1.70 36.086 0 1 1 0 0 2 0 0 0.33 11.678 0 3 0 0 1 3 1 0 1.80 51.612 0 1 1 0 0 3 0 . 0.40 27.664 1 3 0 0 1 3 . . 2.40 1.579 1 3 0 0 1 . 1 0 4.98 45.592 0 3 0 0 1 4 1 . 1.50 187.993 0 3 0 0 1 2 0 0 0.30 -0.033 0 3 0 0 1 . 1 . 1.20 . 0 4 0 0 0 4 . 0 0.70 25.033 1 2 0 1 0 4 1 . 2.40 38.717 0 1 1 0 0 3 0 . 0.40 93.717 0 1 1 0 0 3 1 . 0.81 25.164 1 1 1 0 0 4 0 0 2.60 116.020 0 1 1 0 0 4 1 0 1.00 10.493 1 3 0 0 1 2 0 0 0.26 89.868 0 2 0 1 0 3 1 0 0.72 36.842 0 2 0 1 0 2 1 0 0.35 49.868 0 2 0 1 0 4 1 0 2.40 0.855 0 1 1 0 0 . 0 . 0.36 16.941 0 2 0 1 0 3 1 1 1.55 88.421 0 2 0 1 0 3 1 0 1.20 7.763 0 1 1 0 0 2 0 0 0.41 0.000 0 2 0 1 0 2 0 0 0.45 10.099 0 2 0 1 0 2 0 0 0.35 26.118 1 1 1 0 0 4 1 0 2.76 0.395 0 3 0 0 1 . 1 0 4.59 0.625 0 2 0 1 0 2 1 . 0.44 77.895 0 2 0 1 0 2 0 0 0.33 79.770 0 3 0 0 1 3 1 1 1.20 20.329 0 3 0 0 1 3 1 1 1.28 . 0 4 0 0 0 4 1 0 1.20 -0.033 0 3 0 0 1 4 1 0 3.50 40.724 0 1 1 0 0 3 1 0 0.95 0.230 0 2 0 1 0 3 1 . 0.80 30.921 1 2 0 1 0 4 1 . 2.00 88.618 0 1 1 0 0 3 1 0 1.10 73.257 0 2 0 1 0 4 1 0 1.45 0.000 0 1 1 0 0 2 1 0 0.40 76.316 0 3 0 0 1 3 1 1 1.70 56.250 0 1 1 0 0 3 0 0 0.57 134.967 0 1 1 0 0 2 0 0 0.30 . 0 4 0 0 0 3 . . 0.90 9.408 0 2 0 1 0 2 0 . 0.74 5.197 0 3 0 0 1 . 0 1 8.10 79.145 0 1 1 0 0 4 1 0 1.30 77.829 0 3 0 0 1 . 1 0 1.20 62.204 0 1 1 0 0 3 0 0 0.80 46.842 1 2 0 1 0 2 1 . 0.30 88.586 0 1 1 0 0 . 0 0 4.68 13.125 0 2 0 1 0 4 0 0 1.25 0.625 0 2 0 1 0 4 1 0 1.20 67.072 0 1 1 0 0 4 0 1 2.20 78.520 0 3 0 0 1 4 1 0 1.48 57.007 1 1 1 0 0 4 1 . 1.50 0.625 0 1 1 0 0 3 1 . 0.90 20.428 0 2 0 1 0 4 1 1 7.60 91.645 1 2 0 1 0 3 0 . 3.30 98.355 0 1 1 0 0 3 0 1 0.90 0.822 0 2 0 1 0 3 1 . 0.70 13.849 1 2 0 1 0 3 1 1 1.70 0.822 0 2 0 1 0 2 0 0 0.43 60.132 0 1 1 0 0 2 1 . 0.60 6.776 1 1 1 0 0 . 0 1 3.90 42.138 0 1 1 0 0 4 1 0 2.24 72.796 0 2 0 1 0 2 0 0 0.85 76.908 0 2 0 1 0 3 0 0 1.80 61.743 0 2 0 1 0 3 0 1 2.20 13.586 0 2 0 1 0 4 0 . 3.01 84.704 0 1 1 0 0 2 0 0 0.28 90.428 1 1 1 0 0 4 0 0 1.85 78.651 0 3 0 0 1 2 0 0 0.28 55.164 1 2 0 1 0 4 . 0 0.75 80.625 0 1 1 0 0 3 0 1 1.20 3.882 0 1 1 0 0 3 0 . 0.57 46.283 0 1 1 0 0 2 1 0 0.45 4.145 0 2 0 1 0 4 1 1 0.72 58.980 0 2 0 1 0 4 1 0 0.92 51.645 0 3 0 0 1 4 0 0 1.69 32.961 0 1 1 0 0 3 0 0 0.40 51.349 0 2 0 1 0 4 0 0 1.60 59.803 0 1 1 0 0 3 0 0 1.20 103.618 1 1 1 0 0 4 0 0 2.50 11.020 0 2 0 1 0 2 0 . 0.30 80.757 0 2 0 1 0 3 0 0 1.40 7.072 1 3 0 0 1 . 0 0 5.50 50.493 1 1 1 0 0 2 1 . 1.50 41.711 0 1 1 0 0 4 1 . 1.46 1.513 0 2 0 1 0 4 1 1 4.00 13.750 1 1 1 0 0 4 1 1 1.95 13.520 0 1 1 0 0 3 1 0 1.30 31.612 0 2 0 1 0 2 0 0 0.30 2.434 0 1 1 0 0 2 0 0 0.55 54.079 0 1 1 0 0 2 0 0 0.60 72.928 0 2 0 1 0 4 0 1 6.00 13.026 1 2 0 1 0 4 1 1 4.20 103.618 1 2 0 1 0 3 0 0 0.45 27.434 0 1 1 0 0 3 1 0 0.65 70.395 0 1 1 0 0 4 1 1 2.77 25.559 1 1 1 0 0 4 1 1 2.50 68.322 0 1 1 0 0 3 0 0 1.55 19.803 1 1 1 0 0 4 0 . 3.00 45.461 0 1 1 0 0 2 1 0 0.55 64.901 0 2 0 1 0 3 1 0 0.55 72.697 1 1 1 0 0 3 1 0 0.62 25.625 0 3 0 0 1 2 0 1 0.68 65.230 0 3 0 0 1 3 1 0 1.60 22.993 1 2 0 1 0 3 1 1 1.80 18.421 1 3 0 0 1 4 1 1 5.55 7.171 0 1 1 0 0 3 0 0 0.75 3.914 0 3 0 0 1 3 1 0 0.93 9.112 1 2 0 1 0 3 1 . 1.98 58.125 1 2 0 1 0 3 0 0 1.90 60.559 0 3 0 0 1 . 1 0 2.70 1.118 0 2 0 1 0 3 1 0 0.60 38.717 0 1 1 0 0 3 1 0 0.40 30.724 1 1 1 0 0 2 0 0 0.25 28.684 0 1 1 0 0 4 0 0 1.65 30.230 1 1 1 0 0 3 0 1 4.00 112.829 1 3 0 0 1 4 1 0 0.80 3.355 0 3 0 0 1 2 0 0 0.47 5.855 1 1 1 0 0 3 1 0 1.00 83.388 0 1 1 0 0 2 0 0 0.41 71.151 0 2 0 1 0 3 1 0 1.30 6.908 0 1 1 0 0 2 1 0 0.41 87.533 0 3 0 0 1 2 1 0 0.50 86.382 1 2 0 1 0 2 1 0 0.54 37.895 0 2 0 1 0 4 1 1 3.60 32.829 1 1 1 0 0 3 0 0 0.87 34.145 0 1 1 0 0 3 0 . 0.93 1.743 0 1 1 0 0 3 1 0 2.50 37.303 0 2 0 1 0 4 1 0 2.80 72.237 0 2 0 1 0 3 0 0 0.55 62.763 0 1 1 0 0 2 0 0 0.31 11.053 1 4 0 0 0 4 . . 3.00 21.974 0 1 1 0 0 4 0 . 3.00 54.572 1 1 1 0 0 3 1 1 4.00 25.855 0 2 0 1 0 4 1 0 2.28 56.217 0 2 0 1 0 3 1 0 5.50 37.632 0 3 0 0 1 4 1 . 3.00 68.224 0 2 0 1 0 4 1 0 2.50 62.533 0 3 0 0 1 2 0 0 0.35 2.204 0 2 0 1 0 2 0 . 0.30 61.086 0 2 0 1 0 3 1 0 0.74 25.263 0 2 0 1 0 3 0 0 0.90 39.605 0 2 0 1 0 4 1 . 1.31 24.671 1 2 0 1 0 2 0 0 0.33 14.539 0 3 0 0 1 3 0 0 0.82 13.322 0 1 1 0 0 3 0 0 1.00 10.855 1 1 1 0 0 4 1 0 2.00 10.329 0 2 0 1 0 3 1 1 1.50 0.987 0 2 0 1 0 4 0 0 0.85 0.461 0 3 0 0 1 4 1 0 2.00 20.263 0 2 0 1 0 3 1 . 7.10 14.770 0 3 0 0 1 3 0 0 0.60 0.493 0 2 0 1 0 2 1 0 0.30 58.355 0 1 1 0 0 2 0 0 0.20 72.368 0 2 0 1 0 4 1 0 1.33 86.316 0 2 0 1 0 4 1 1 2.75 5.033 1 2 0 1 0 . 1 1 7.00 57.632 0 2 0 1 0 4 1 0 0.68 102.204 1 1 1 0 0 3 0 . 0.98 77.862 0 1 1 0 0 3 0 0 0.80 62.401 0 2 0 1 0 4 1 0 2.00 60.789 0 1 1 0 0 3 0 0 1.10 70.230 0 2 0 1 0 3 0 . 0.55 42.796 1 1 1 0 0 4 0 0 2.10 4.638 0 4 0 0 0 2 0 0 0.50 177.072 0 1 1 0 0 3 0 . 0.74 22.829 0 1 1 0 0 4 1 0 2.75 90.559 1 2 0 1 0 3 0 0 1.30 0.000 0 3 0 0 1 4 1 . 2.80 24.539 1 4 0 0 0 4 0 1 1.79 35.789 1 2 0 1 0 4 0 1 4.15 58.257 1 1 1 0 0 . 0 0 10.00 25.329 1 4 0 0 0 . 1 . 4.00 109.243 1 2 0 1 0 4 1 1 4.00 73.355 1 1 1 0 0 4 0 . 1.28 112.566 0 2 0 1 0 2 0 0 0.42 6.151 0 2 0 1 0 3 0 0 1.00 6.086 1 1 1 0 0 4 1 0 2.15 27.336 0 3 0 0 1 3 0 . 2.57 13.914 0 2 0 1 0 3 1 0 0.95 38.421 0 1 1 0 0 4 1 . 2.70 67.467 0 2 0 1 0 3 1 0 1.20 16.612 1 2 0 1 0 4 1 1 1.27 5.691 1 1 1 0 0 3 1 0 1.45 16.349 0 3 0 0 1 2 0 0 0.40 61.382 1 1 1 0 0 . 0 1 5.50 10.362 0 1 1 0 0 3 0 0 2.00 21.612 0 3 0 0 1 3 1 1 1.79 16.118 1 1 1 0 0 . 0 0 9.50 0.296 0 2 0 1 0 2 1 0 0.25 65.592 0 2 0 1 0 3 0 1 2.50 11.974 0 2 0 1 0 3 0 0 2.00 14.638 0 2 0 1 0 2 1 0 0.36 34.803 1 2 0 1 0 4 1 0 2.71 13.059 1 4 0 0 0 . 0 1 7.00 11.316 0 2 0 1 0 3 0 . 0.92 1.349 1 2 0 1 0 2 1 0 0.70 0.461 0 1 1 0 0 2 0 0 0.55 1.283 0 2 0 1 0 3 1 0 0.62 43.355 0 2 0 1 0 3 1 0 0.65 24.211 0 1 1 0 0 4 0 0 0.76 30.033 0 2 0 1 0 4 0 0 1.57 44.737 1 2 0 1 0 3 1 0 0.60 27.533 1 2 0 1 0 4 0 0 1.70 2.533 0 3 0 0 1 4 1 . 1.84 8.651 1 2 0 1 0 . 1 0 . 27.664 0 2 0 1 0 4 0 0 1.83 107.270 0 2 0 1 0 2 0 . 0.37 5.263 0 3 0 0 1 3 1 0 1.02 14.079 1 2 0 1 0 4 1 0 3.75 51.974 0 2 0 1 0 4 1 0 1.60 26.480 0 1 1 0 0 3 0 0 3.00 119.638 0 2 0 1 0 2 0 0 0.31 8.651 1 2 0 1 0 4 1 1 4.80 61.612 1 1 1 0 0 3 1 0 1.30 106.086 0 3 0 0 1 4 0 0 2.00 59.145 0 2 0 1 0 4 1 0 1.15 61.678 1 2 0 1 0 3 0 . 4.00 23.026 0 2 0 1 0 4 1 1 4.00 49.474 0 2 0 1 0 2 . 0 0.41 0.428 0 3 0 0 1 4 1 1 10.00 7.105 0 3 0 0 1 1 0 0 0.79 32.862 0 1 1 0 0 3 0 . 0.58 50.921 0 2 0 1 0 4 0 0 1.34 1.053 0 2 0 1 0 2 1 0 0.46 15.395 0 1 1 0 0 4 0 0 4.20 1.513 0 3 0 0 1 2 0 0 0.70 7.796 1 3 0 0 1 . 0 0 20.00 9.967 1 2 0 1 0 4 1 1 5.00 44.178 0 3 0 0 1 3 0 0 0.51 45.329 0 2 0 1 0 3 0 1 1.74 15.066 0 4 0 0 0 2 1 0 0.50 34.901 1 3 0 0 1 4 1 . 2.60 36.480 0 4 0 0 0 2 0 . 0.40 22.368 0 1 1 0 0 2 1 0 0.30 13.191 0 2 0 1 0 4 0 0 0.98 61.678 0 2 0 1 0 4 1 0 2.84 60.395 0 1 1 0 0 4 0 0 1.73 18.553 0 1 1 0 0 4 1 0 1.68 3.125 0 2 0 1 0 2 0 0 0.63 34.211 0 2 0 1 0 3 1 0 1.10 . 0 3 0 0 1 4 1 . 2.90 12.007 1 3 0 0 1 . 1 . 0.90 1.875 0 2 0 1 0 4 1 0 2.04 32.763 1 2 0 1 0 3 1 0 1.55 43.092 1 2 0 1 0 4 1 0 4.00 66.053 1 2 0 1 0 3 1 . 2.50 11.546 1 1 1 0 0 3 1 0 1.20 36.151 0 3 0 0 1 3 1 1 2.32 6.480 0 2 0 1 0 4 1 1 5.00 42.138 0 1 1 0 0 2 1 0 0.51 3.882 0 4 0 0 0 . 0 1 1.50 1.974 0 1 1 0 0 4 0 1 2.80 12.796 0 1 1 0 0 4 0 0 1.50 16.151 0 2 0 1 0 2 1 . 0.31 1.184 0 2 0 1 0 2 1 0 0.13 24.375 0 3 0 0 1 4 1 0 5.10 6.875 1 1 1 0 0 4 1 0 3.40 8.454 1 2 0 1 0 . 1 0 3.70 60.000 0 2 0 1 0 3 1 0 0.97 49.375 0 1 1 0 0 2 0 1 0.30 102.204 0 2 0 1 0 3 0 0 1.50 36.579 1 1 1 0 0 3 0 . 2.80 58.322 0 1 1 0 0 4 1 0 2.00 17.204 0 1 1 0 0 2 1 0 0.35 19.901 0 2 0 1 0 3 1 0 0.54 15.822 0 2 0 1 0 4 1 0 5.40 6.447 1 4 0 0 0 4 1 1 3.25 0.559 0 2 0 1 0 4 . 1 1.10 11.382 1 2 0 1 0 4 1 1 3.45 71.118 1 1 1 0 0 . 1 . 1.23 46.908 0 3 0 0 1 4 1 0 5.05 18.914 1 2 0 1 0 4 1 0 1.54 30.954 1 2 0 1 0 . 0 . 1.94 57.105 0 1 1 0 0 3 0 0 0.50 30.362 0 2 0 1 0 4 1 0 1.40 50.263 0 3 0 0 1 4 1 1 2.73 0.461 0 1 1 0 0 . 1 0 4.29 32.533 0 2 0 1 0 4 0 1 0.75 14.803 0 2 0 1 0 4 1 0 2.00 46.776 0 1 1 0 0 4 1 0 2.64 5.954 1 1 1 0 0 4 0 0 2.40 42.434 1 2 0 1 0 3 0 0 1.71 7.105 0 3 0 0 1 3 1 0 0.40 89.836 0 1 1 0 0 2 1 0 0.36 40.230 0 2 0 1 0 3 1 0 0.40 11.645 0 1 1 0 0 3 0 0 0.92 36.941 0 2 0 1 0 4 0 0 0.90 87.2039 1 4 0 0 0 3 0 0 2.10 58.4211 0 1 1 0 0 4 0 0 4.10 31.7763 1 3 0 0 1 4 1 . 1.80 48.2566 0 1 1 0 0 4 0 0 0.89 83.2237 0 2 0 1 0 4 0 . 2.70 49.0789 0 1 1 0 0 4 1 1 3.00 15.4605 0 1 1 0 0 2 0 0 0.49 6.2171 0 2 0 1 0 4 1 0 1.50 3.3882 0 1 1 0 0 2 0 0 0.75 0.6250 0 2 0 1 0 2 0 0 0.40 0.3947 0 4 0 0 0 4 0 1 2.50 49.5395 0 1 1 0 0 2 1 0 0.35 57.8289 0 4 0 0 0 3 0 0 1.00 29.7039 1 2 0 1 0 3 0 0 2.00 38.9145 0 2 0 1 0 3 1 0 0.97 24.5724 0 1 1 0 0 4 0 1 4.75 2.9934 0 2 0 1 0 3 1 0 0.62 38.4868 0 2 0 1 0 3 1 0 0.50 41.3158 0 2 0 1 0 2 0 0 0.45 18.8487 0 3 0 0 1 2 1 0 0.32 2.0724 0 1 1 0 0 . 1 1 3.55 40.1974 0 2 0 1 0 4 1 1 1.60 28.5526 1 2 0 1 0 4 1 . 1.72 8.8816 1 2 0 1 0 4 1 0 1.50 30.0658 0 2 0 1 0 3 0 0 0.76 55.3947 0 2 0 1 0 3 0 0 1.58 29.4408 0 4 0 0 0 . 0 0 1.60 0.6250 0 2 0 1 0 3 0 0 0.82 46.6776 0 3 0 0 1 3 1 0 0.45 20.8553 0 2 0 1 0 2 0 0 0.65 49.7039 0 2 0 1 0 4 1 0 1.31 1.1513 0 1 1 0 0 2 1 0 0.46 32.6316 0 2 0 1 0 4 1 1 2.90 30.4276 1 1 1 0 0 4 1 0 2.60 57.5987 0 2 0 1 0 4 1 0 2.10 13.8816 1 2 0 1 0 3 1 0 2.60 53.0921 0 4 0 0 0 3 1 1 2.19 2.796 0 2 0 1 0 2 1 0 0.40 37.796 0 3 0 0 1 3 1 0 0.55 50.230 0 1 1 0 0 4 1 0 1.12 44.572 0 3 0 0 1 . 1 0 5.10 37.401 0 3 0 0 1 3 0 0 0.90 21.809 1 3 0 0 1 2 1 0 0.67 0.461 0 2 0 1 0 3 0 0 0.55 18.454 0 2 0 1 0 4 0 1 5.12 2.961 0 2 0 1 0 2 1 0 0.20 46.776 0 3 0 0 1 4 1 1 2.20 105.493 1 1 1 0 0 4 0 1 3.30 57.401 0 3 0 0 1 4 1 0 1.40 39.112 0 3 0 0 1 3 1 0 1.10 46.283 0 3 0 0 1 2 1 0 0.10 27.401 1 1 1 0 0 3 0 0 1.20 11.283 0 1 1 0 0 3 0 0 0.33 5.000 1 1 1 0 0 4 0 1 2.44 47.730 0 1 1 0 0 4 0 0 2.55 47.434 0 2 0 1 0 4 1 0 2.90 0.592 0 4 0 0 0 . 1 1 5.00 1.842 0 1 1 0 0 4 0 0 1.54 50.230 0 1 1 0 0 3 1 0 0.78 12.237 0 1 1 0 0 3 0 0 0.60 45.724 1 3 0 0 1 4 1 0 2.54 47.566 0 3 0 0 1 2 1 0 1.20 0.526 0 2 0 1 0 3 1 0 0.91 8.618 1 2 0 1 0 3 1 1 2.91 41.612 0 2 0 1 0 4 1 0 1.32 46.053 0 2 0 1 0 4 0 . 3.00 52.270 0 1 1 0 0 3 0 0 0.65 3.914 0 1 1 0 0 2 1 0 0.35 0.493 1 3 0 0 1 4 1 . 2.50 51.151 0 3 0 0 1 4 1 0 1.72 28.257 0 3 0 0 1 4 1 . 3.00 53.849 0 2 0 1 0 3 0 0 1.50 17.862 0 3 0 0 1 4 0 1 4.85 23.224 1 2 0 1 0 4 0 0 1.62 53.651 0 3 0 0 1 3 1 0 0.98 48.586 0 1 1 0 0 1 0 0 0.50 15.395 0 2 0 1 0 3 1 0 1.27 45.724 0 2 0 1 0 3 1 0 1.30 32.401 1 1 1 0 0 3 0 0 1.40 30.296 0 1 1 0 0 4 1 1 2.31 95.000 0 2 0 1 0 4 1 0 1.20 16.151 0 2 0 1 0 3 1 1 0.80 4.145 0 2 0 1 0 4 0 1 2.20 30.197 0 2 0 1 0 2 1 0 1.14 34.539 0 1 1 0 0 3 0 0 0.62 0.395 0 3 0 0 1 4 1 0 1.35 38.684 0 2 0 1 0 2 1 0 1.87 60.559 1 4 0 0 0 2 1 0 0.48 47.664 1 3 0 0 1 4 0 0 0.95 18.651 1 2 0 1 0 4 1 0 4.00 21.250 1 2 0 1 0 3 1 0 2.64 20.658 1 2 0 1 0 . 1 1 6.47 34.671 0 1 1 0 0 4 1 0 3.89 23.059 1 1 1 0 0 3 1 0 1.17 29.112 0 1 1 0 0 3 0 0 0.76 9.474 1 2 0 1 0 4 1 0 4.34 215.789 0 3 0 0 1 4 1 0 1.40 40.132 0 2 0 1 0 . 1 1 3.50 27.599 1 3 0 0 1 4 1 0 2.55 25.362 0 1 1 0 0 3 1 0 1.80 0.888 0 2 0 1 0 2 1 0 0.50 7.796 1 2 0 1 0 4 1 0 1.53 48.816 0 4 0 0 0 4 1 0 1.28 11.842 1 2 0 1 0 4 1 . 5.00 24.901 0 2 0 1 0 4 1 0 1.55 59.112 0 1 1 0 0 3 0 0 0.79 47.533 1 2 0 1 0 4 0 1 2.30 56.316 0 2 0 1 0 2 1 0 0.60 30.132 0 2 0 1 0 4 1 1 2.50 50.658 0 1 1 0 0 2 0 . 0.39 0.559 0 2 0 1 0 2 1 0 0.50 40.329 0 2 0 1 0 3 0 0 0.48 25.691 1 2 0 1 0 3 1 0 1.72 2.105 0 1 1 0 0 2 0 0 0.74 7.138 0 1 1 0 0 3 0 0 0.47 55.789 0 2 0 1 0 4 0 0 0.92 10.625 1 1 1 0 0 4 0 1 2.20 13.750 1 2 0 1 0 4 1 1 4.40 44.507 1 1 1 0 0 4 0 0 1.78 22.697 1 2 0 1 0 4 0 0 2.30 4.178 0 3 0 0 1 4 1 1 3.16 0.789 0 1 1 0 0 . 0 . 10.00 4.112 1 1 1 0 0 3 1 1 2.30 9.868 0 2 0 1 0 3 0 . 0.63 0.428 0 1 1 0 0 3 0 1 1.66 26.283 0 2 0 1 0 3 0 0 1.43 54.145 0 3 0 0 1 3 1 0 1.95 0.921 0 1 1 0 0 4 0 1 12.10 0.888 0 3 0 0 1 4 1 1 3.17 24.243 0 1 1 0 0 3 0 1 0.88 42.401 0 1 1 0 0 2 0 0 0.50 11.086 1 3 0 0 1 4 1 0 2.55 12.138 0 1 1 0 0 4 0 0 1.46 48.092 0 2 0 1 0 4 0 1 1.43 44.803 0 2 0 1 0 3 0 1 0.80 22.303 1 2 0 1 0 4 1 0 1.70 36.118 0 2 0 1 0 3 1 0 0.85 38.553 0 1 1 0 0 4 0 0 2.20 79.079 0 2 0 1 0 3 1 . 0.50 16.711 1 2 0 1 0 3 1 . 1.71 5.691 1 3 0 0 1 . 1 . 0.75 152.401 0 2 0 1 0 3 1 . 0.75 18.553 1 1 1 0 0 4 0 1 1.80 0.493 0 2 0 1 0 4 0 1 2.55 10.296 0 3 0 0 1 4 1 1 3.16 39.079 0 1 1 0 0 3 1 . 0.50 35.625 0 3 0 0 1 3 1 . 0.62 23.026 1 2 0 1 0 3 1 . 0.40 36.414 0 1 1 0 0 4 1 0 0.90 70.033 0 2 0 1 0 2 0 0 0.39 44.046 0 2 0 1 0 3 0 0 1.02 21.151 1 2 0 1 0 3 1 . 1.60 47.204 0 1 1 0 0 . 0 1 3.80 2.072 0 1 1 0 0 2 1 . 0.60 0.461 0 1 1 0 0 2 1 . 0.26 3.684 1 2 0 1 0 4 1 . 3.83 36.414 0 3 0 0 1 3 0 0 1.56 31.053 0 2 0 1 0 2 1 0 0.60 4.638 0 1 1 0 0 2 1 0 0.53 16.678 0 2 0 1 0 3 1 . 0.85 0.921 0 3 0 0 1 3 1 . 0.70 21.743 0 1 1 0 0 3 0 0 1.10 17.895 0 1 1 0 0 3 1 1 2.89 15.132 1 2 0 1 0 3 0 0 1.30 14.967 1 3 0 0 1 2 1 0 0.60 42.138 0 1 1 0 0 3 0 . 1.00 19.474 0 2 0 1 0 2 0 . 0.33 8.783 0 3 0 0 1 . 0 1 8.00 45.789 0 2 0 1 0 3 1 0 1.80 10.197 0 2 0 1 0 2 1 0 0.46 35.099 0 2 0 1 0 3 1 0 1.65 21.809 1 3 0 0 1 . 1 1 6.00 8.618 1 3 0 0 1 4 1 . 0.90 43.684 0 1 1 0 0 3 0 0 0.72 152.730 0 1 1 0 0 4 0 1 1.32 7.336 0 2 0 1 0 4 0 1 1.67 18.191 0 1 1 0 0 4 0 0 1.10 2.368 0 2 0 1 0 3 1 0 1.35 30.888 0 2 0 1 0 3 0 . 0.60 34.013 0 1 1 0 0 3 0 0 0.80 37.039 0 1 1 0 0 3 0 . 1.15 21.414 1 1 1 0 0 3 1 1 4.00 6.809 0 2 0 1 0 2 1 . 0.30 11.612 0 1 1 0 0 4 0 . 2.00 26.118 0 2 0 1 0 3 1 . 0.77 5.296 1 3 0 0 1 3 1 0 1.67 15.526 0 1 1 0 0 2 0 0 0.36 20.428 1 2 0 1 0 4 1 1 1.90 18.947 1 3 0 0 1 3 1 . 0.90 0.296 0 1 1 0 0 3 1 0 0.67 0.625 0 3 0 0 1 4 1 1 5.50 35.395 0 3 0 0 1 2 1 . 0.41 17.730 0 1 1 0 0 2 0 . 0.30 25.099 0 3 0 0 1 4 1 0 0.80 93.882 0 2 0 1 0 3 1 0 0.75 48.750 0 2 0 1 0 2 1 . 0.54 45.888 0 3 0 0 1 3 1 . 2.30 40.099 1 2 0 1 0 3 0 0 0.96 59.770 1 2 0 1 0 4 0 1 4.10 43.454 0 1 1 0 0 2 0 . 0.42 29.211 1 2 0 1 0 4 1 1 5.50 4.901 0 1 1 0 0 3 0 . 0.95 0.658 0 2 0 1 0 2 1 0 0.46 61.250 0 1 1 0 0 4 0 . 1.20 0.132 0 3 0 0 1 2 1 0 0.35 9.671 1 3 0 0 1 . 1 1 3.90 16.941 1 2 0 1 0 4 1 . 2.90 5.493 0 2 0 1 0 2 1 0 0.34 30.263 0 2 0 1 0 4 1 . 2.10 134.934 0 2 0 1 0 2 0 . 0.40 37.039 0 1 1 0 0 3 0 . 2.55 5.592 0 2 0 1 0 4 1 . 1.32 37.105 0 2 0 1 0 2 1 0 0.31 176.875 0 2 0 1 0 3 1 . 1.10 11.546 1 2 0 1 0 3 1 1 2.86 34.079 1 4 0 0 0 4 0 . 1.60 44.408 0 2 0 1 0 4 1 . 7.40 29.934 1 1 1 0 0 . 0 1 3.40 35.888 0 1 1 0 0 2 0 0 0.40 5.987 1 1 1 0 0 4 1 . 1.30 2.566 0 2 0 1 0 4 0 1 2.50 36.151 0 1 1 0 0 2 1 . 0.26 31.4145 0 2 0 1 0 3 1 0 0.71 23.4868 0 2 0 1 0 3 0 0 0.84 59.1118 1 2 0 1 0 3 1 0 0.70 4.7697 0 2 0 1 0 2 0 0 0.36 36.8750 0 2 0 1 0 3 1 . 2.10 3.6513 0 3 0 0 1 4 1 . 1.68 12.5329 0 2 0 1 0 3 1 0 0.70 6.6118 0 2 0 1 0 2 0 . 0.30 37.7632 0 2 0 1 0 4 1 1 3.00 3.8816 0 3 0 0 1 4 1 . 5.10 0.2632 0 4 0 0 0 3 1 1 2.20 36.4145 1 2 0 1 0 4 1 0 3.10 37.7632 0 3 0 0 1 4 0 0 2.40 0.4934 0 2 0 1 0 4 1 1 3.80 20.8553 1 3 0 0 1 4 0 . 3.60 1.6447 1 2 0 1 0 4 0 0 2.05 35.5263 0 1 1 0 0 3 1 0 0.83 1.9737 0 2 0 1 0 4 1 0 1.73 35.2303 0 2 0 1 0 4 1 1 2.80 8.5197 0 1 1 0 0 3 0 1 1.05 40.4605 0 1 1 0 0 3 1 0 0.68 8.0921 0 2 0 1 0 2 1 . 0.35 50.4605 1 2 0 1 0 4 1 0 1.24 11.1184 0 2 0 1 0 3 0 0 1.10 29.2763 0 2 0 1 0 4 1 0 2.38 0.3947 0 3 0 0 1 4 1 . 0.90 36.4145 0 2 0 1 0 4 1 . 3.20 6.2500 1 1 1 0 0 . 1 1 12.00 3.5526 0 2 0 1 0 4 1 0 1.45 23.3553 0 2 0 1 0 3 1 0 0.65 0.0000 0 2 0 1 0 2 1 0 0.40 0.0000 0 1 1 0 0 . 1 0 0.40 11.3487 1 2 0 1 0 4 0 0 1.87 35.4605 0 1 1 0 0 2 1 0 0.48 30.6250 0 3 0 0 1 4 1 . 0.90 28.9474 0 1 1 0 0 2 0 0 0.18 14.4079 0 2 0 1 0 4 1 1 2.30 64.474 0 3 0 0 1 2 1 0 0.40 8.289 0 2 0 1 0 4 1 1 1.78 49.375 1 2 0 1 0 4 1 . 4.30 5.164 0 1 1 0 0 2 0 0 0.27 2.336 0 4 0 0 0 2 1 0 0.45 48.553 0 1 1 0 0 2 1 . 0.35 3.750 0 1 1 0 0 2 1 . 0.32 30.559 0 1 1 0 0 4 1 . 1.48 37.993 0 1 1 0 0 2 1 0 0.63 2.599 0 1 1 0 0 2 0 . 0.40 79.803 0 2 0 1 0 2 1 . 0.40 73.750 1 2 0 1 0 4 0 . 1.90 2.993 0 2 0 1 0 2 0 . 0.28 1.283 0 3 0 0 1 2 0 . 0.45 4.967 0 3 0 0 1 4 1 1 4.10 0.724 0 1 1 0 0 3 0 . 1.30 0.789 0 1 1 0 0 3 0 . 1.15 19.605 0 3 0 0 1 2 1 . 0.55 35.592 0 1 1 0 0 2 0 . 0.40 32.664 0 1 1 0 0 . 1 1 5.00 35.691 0 1 1 0 0 4 1 . 2.10 7.368 0 2 0 1 0 3 1 . 2.25 3.257 0 1 1 0 0 4 0 1 4.50 101.908 0 2 0 1 0 . 0 . 0.90 38.882 0 1 1 0 0 . 1 . 0.31 5.263 1 1 1 0 0 3 1 1 1.83 3.750 0 2 0 1 0 3 1 0 0.95 36.974 0 4 0 0 0 4 0 . 1.50 25.428 0 1 1 0 0 4 0 0 0.87 22.007 0 2 0 1 0 4 1 0 3.08 35.921 0 1 1 0 0 4 0 0 2.00 36.414 0 3 0 0 1 . 0 0 2.62 37.138 0 1 1 0 0 4 1 1 2.62 48.717 1 2 0 1 0 3 1 0 1.86 37.401 0 2 0 1 0 4 1 0 1.52 0.855 0 2 0 1 0 4 0 . 1.15 3.191 0 1 1 0 0 4 0 1 2.52 35.6908 0 1 1 0 0 . 0 1 1.90 41.4145 0 2 0 1 0 4 1 . 1.80 13.1250 0 1 1 0 0 4 0 0 1.07 33.7500 0 1 1 0 0 2 1 0 0.24 31.4803 0 4 0 0 0 3 1 . 0.80 36.4474 0 2 0 1 0 4 1 1 5.20 18.1250 1 1 1 0 0 . 1 1 6.00 0.2303 0 1 1 0 0 3 0 0 0.80 29.9013 0 3 0 0 1 4 0 . 2.10 2.2697 0 2 0 1 0 . 1 . 11.00 4.4079 0 3 0 0 1 4 1 1 1.75 36.0526 0 2 0 1 0 3 0 0 0.65 66.3816 0 1 1 0 0 . 0 . 8.00 7.8289 0 2 0 1 0 2 0 . 0.25 27.5658 0 1 1 0 0 2 0 . 0.38 11.6776 0 2 0 1 0 3 1 . 1.77 30.9539 0 2 0 1 0 2 1 0 0.80 30.6579 0 3 0 0 1 4 0 . 1.56 36.1842 0 2 0 1 0 4 1 . 2.90 35.2961 0 1 1 0 0 4 0 . 1.45 29.0461 0 2 0 1 0 4 1 . 2.00 84.4408 1 3 0 0 1 3 1 . 0.85 12.8947 1 3 0 0 1 4 0 1 2.11 8.0263 1 3 0 0 1 . 1 . 11.00 11.2171 1 1 1 0 0 4 1 1 3.70 38.2895 1 2 0 1 0 3 1 0 1.40 21.7105 1 1 1 0 0 . 0 . 3.40 36.1842 0 1 1 0 0 4 1 0 2.10 10.1645 1 1 1 0 0 4 0 . 1.50 27.0724 0 2 0 1 0 3 0 . 0.90 1.7763 0 3 0 0 1 2 1 0 0.43 34.3421 0 3 0 0 1 2 1 0 0.42 2.5000 0 1 1 0 0 4 1 . 5.38 33.3882 0 3 0 0 1 4 0 0 1.28 34.0789 0 3 0 0 1 2 0 1 2.00 34.5066 0 2 0 1 0 3 0 0 1.15 20.4276 1 2 0 1 0 4 1 0 2.30 22.599 0 3 0 0 1 1 1 0 0.66 35.888 0 1 1 0 0 4 1 . 1.23 3.947 0 2 0 1 0 2 1 0 0.70 3.783 0 2 0 1 0 2 0 0 0.30 62.730 1 1 1 0 0 4 1 0 1.68 18.191 0 2 0 1 0 3 0 1 1.60 0.888 0 1 1 0 0 4 0 . 4.40 1.546 0 3 0 0 1 . 1 0 5.94 1.612 0 2 0 1 0 4 1 0 1.40 22.303 0 2 0 1 0 3 1 . 2.20 29.474 0 3 0 0 1 3 1 . 0.92 15.164 1 2 0 1 0 4 1 1 1.80 132.171 0 1 1 0 0 . 0 . 2.73 3.717 1 1 1 0 0 . 0 1 13.00 34.737 0 1 1 0 0 2 1 0 0.60 18.914 0 2 0 1 0 2 1 . 0.35 27.961 1 1 1 0 0 4 1 0 0.75 30.428 0 2 0 1 0 2 1 . 1.05 7.632 0 1 1 0 0 3 1 0 0.86 6.809 1 1 1 0 0 . 1 1 4.62 3.750 0 1 1 0 0 3 0 0 2.20 0.691 0 1 1 0 0 4 1 . 2.50 18.224 0 2 0 1 0 2 0 0 0.64 19.803 1 1 1 0 0 . 0 . 3.30 16.875 0 1 1 0 0 2 0 0 0.40 0.000 1 1 1 0 0 4 0 . 1.21 23.289 0 2 0 1 0 2 1 0 0.22 28.586 0 2 0 1 0 2 1 0 0.27 7.072 0 2 0 1 0 2 1 0 0.50 123.289 0 2 0 1 0 3 0 . 1.75 24.375 1 1 1 0 0 4 0 0 4.42 13.125 1 3 0 0 1 3 0 1 2.55 0.921 0 1 1 0 0 2 1 0 0.81 31.086 0 2 0 1 0 3 1 0 2.80 0.526 0 1 1 0 0 3 1 0 1.58 29.770 0 3 0 0 1 2 1 0 0.35 32.237 0 2 0 1 0 4 0 0 1.10 31.546 0 1 1 0 0 3 0 0 1.10 33.454 0 2 0 1 0 3 1 0 0.86 6.776 0 1 1 0 0 4 0 . 1.08 14.112 1 2 0 1 0 4 1 1 2.55 27.862 0 1 1 0 0 4 0 1 1.78 25.822 0 1 1 0 0 3 0 0 0.76 8.914 0 1 1 0 0 3 0 0 0.80 15.625 1 2 0 1 0 4 0 0 2.00 29.901 0 1 1 0 0 3 0 0 1.58 9.605 0 1 1 0 0 4 0 0 1.25 0.855 0 2 0 1 0 2 0 . 0.50 2.007 0 2 0 1 0 3 1 0 0.85 14.638 0 2 0 1 0 4 0 1 3.00 31.579 0 2 0 1 0 3 0 0 0.72 25.526 0 2 0 1 0 . 1 1 6.00 31.250 0 2 0 1 0 4 1 1 4.80 25.559 1 1 1 0 0 4 1 1 4.55 31.316 0 2 0 1 0 2 1 0 0.24 29.539 0 2 0 1 0 4 1 0 1.20 33.125 1 2 0 1 0 3 0 . 1.56 5.987 0 2 0 1 0 2 1 . 0.85 35.033 1 2 0 1 0 3 1 . 1.04 129.638 1 2 0 1 0 4 1 . 2.00 4.671 1 3 0 0 1 4 1 . 1.50 11.579 1 2 0 1 0 4 1 . 1.87 6.579 0 2 0 1 0 4 1 0 3.50 0.987 1 2 0 1 0 . 1 0 1.52 12.467 0 2 0 1 0 2 1 0 0.47 29.309 0 1 1 0 0 4 0 0 1.25 5.395 1 1 1 0 0 3 0 0 1.82 7.664 1 2 0 1 0 4 1 . 3.30 29.671 0 2 0 1 0 3 0 0 1.24 93.026 1 2 0 1 0 2 1 . 0.56 78.026 0 2 0 1 0 2 1 0 0.27 22.467 0 2 0 1 0 4 1 1 5.20 4.934 1 2 0 1 0 4 0 1 1.00 31.645 0 1 1 0 0 2 0 0 0.30 31.3816 0 2 0 1 0 4 0 0 2.10 30.2303 0 1 1 0 0 4 1 0 1.86 2.4013 0 3 0 0 1 4 0 . 2.20 28.7829 0 1 1 0 0 4 0 0 1.75 12.8947 0 2 0 1 0 3 0 . 1.04 79.9342 1 2 0 1 0 4 0 . 2.03 14.2763 0 2 0 1 0 2 1 0 0.50 29.5066 0 2 0 1 0 3 0 0 1.10 13.2895 0 1 1 0 0 4 0 0 2.88 54.1447 0 1 1 0 0 3 1 . 1.32 29.9342 0 1 1 0 0 2 1 0 0.40 31.4145 0 2 0 1 0 2 1 . 0.80 4.1118 0 3 0 0 1 2 1 . 0.35 2.6316 1 2 0 1 0 4 0 1 2.90 6.6118 1 1 1 0 0 4 1 1 8.20 3.5526 0 3 0 0 1 3 1 . 1.25 34.0461 0 3 0 0 1 4 1 0 1.22 28.3553 0 1 1 0 0 2 0 0 0.60 27.1711 0 2 0 1 0 4 0 1 1.60 9.0132 1 2 0 1 0 4 0 0 1.35 67.1053 1 2 0 1 0 4 1 0 2.25 24.0132 0 1 1 0 0 3 0 1 1.82 8.9803 0 1 1 0 0 3 1 0 1.00 4.1447 1 1 1 0 0 4 0 1 . 28.3553 1 1 1 0 0 4 0 1 4.50 19.2434 1 2 0 1 0 3 1 1 2.30 0.2961 0 3 0 0 1 2 0 0 0.72 23.2566 0 2 0 1 0 3 1 . 0.50 0.3618 0 1 1 0 0 3 1 0 0.52 6.8750 1 1 1 0 0 4 0 1 3.00 12.2368 0 2 0 1 0 3 1 1 0.65 26.8421 1 3 0 0 1 3 1 . 0.90 25.0658 0 3 0 0 1 3 1 . 0.48 3.2237 0 1 1 0 0 2 1 1 3.00 24.7039 0 2 0 1 0 3 1 0 0.81 22.0724 0 1 1 0 0 3 1 0 0.92 23.6184 0 1 1 0 0 3 0 1 0.69 19.3421 0 2 0 1 0 3 1 1 3.00 48.0263 0 2 0 1 0 2 1 1 1.30 25.3618 0 3 0 0 1 2 0 0 0.32 66.3158 1 2 0 1 0 4 1 . 3.80 1.6776 1 1 1 0 0 4 1 0 1.74 65.7237 0 3 0 0 1 1 1 . 0.10 13.9803 0 1 1 0 0 4 1 0 6.00 1.3816 0 3 0 0 1 3 1 . 1.00 23.7500 1 2 0 1 0 4 1 . 3.50 0.4276 0 1 1 0 0 . 1 0 0.64 27.2039 1 1 1 0 0 4 0 0 1.76 0.0000 1 1 1 0 0 4 0 . 1.85 64.9671 1 3 0 0 1 3 0 0 3.80 54.1118 1 1 1 0 0 4 1 0 2.40 0.5592 0 2 0 1 0 2 0 . 0.61 28.4868 0 3 0 0 1 4 1 . 1.10 3.1250 0 3 0 0 1 . 1 . 10.60 4.6711 0 1 1 0 0 3 0 . 1.04 26.8750 0 1 1 0 0 4 0 1 2.50 -2.9934 1 2 0 1 0 4 1 0 2.49 26.9408 1 1 1 0 0 3 1 . 1.20 0.0000 1 2 0 1 0 3 1 0 1.00 26.4803 0 3 0 0 1 4 1 1 3.60 44.7039 1 1 1 0 0 3 1 . 2.30 1.2171 1 3 0 0 1 4 1 0 2.70 1.2829 0 2 0 1 0 3 0 0 0.50 0.0000 0 2 0 1 0 3 1 0 7.00 2.3355 0 1 1 0 0 4 1 1 3.00 13.9474 0 4 0 0 0 3 1 0 0.90 1.8421 0 2 0 1 0 3 1 . 0.65 19.0789 0 1 1 0 0 3 1 . 0.50 11.4803 0 1 1 0 0 4 0 0 2.10 28.4868 0 2 0 1 0 4 1 0 4.00 16.7434 0 1 1 0 0 4 0 0 0.90 19.6382 0 1 1 0 0 2 0 0 0.48 28.1579 0 2 0 1 0 3 1 0 0.50 0.1974 0 1 1 0 0 3 0 . 0.82 1.086 0 1 1 0 0 4 1 . 3.00 17.303 0 3 0 0 1 4 1 0 5.50 166.447 1 2 0 1 0 3 1 . 0.81 2.007 1 1 1 0 0 3 1 . 0.67 25.526 0 1 1 0 0 2 0 . 0.50 2.599 0 1 1 0 0 4 0 0 2.55 24.441 0 2 0 1 0 3 1 0 3.17 16.349 1 2 0 1 0 4 0 . 2.20 8.717 0 2 0 1 0 4 1 . 2.40 34.375 0 3 0 0 1 4 1 0 2.02 34.375 0 2 0 1 0 4 1 0 1.02 9.507 0 3 0 0 1 4 1 0 4.10 0.461 0 3 0 0 1 4 1 1 3.00 11.579 0 2 0 1 0 3 1 0 0.94 2.829 0 1 1 0 0 3 1 0 0.51 4.013 0 2 0 1 0 4 0 0 1.44 22.862 0 2 0 1 0 3 1 0 2.30 23.158 0 2 0 1 0 3 1 . 1.35 25.362 0 2 0 1 0 4 1 0 1.65 29.737 0 2 0 1 0 4 0 0 1.10 9.539 0 3 0 0 1 3 1 0 0.90 10.132 1 1 1 0 0 4 0 0 1.70 1.513 0 2 0 1 0 2 0 . 0.25 24.474 0 1 1 0 0 4 0 . 5.50 8.882 0 2 0 1 0 3 . 1 2.05 12.237 1 3 0 0 1 4 1 . 2.00 0.888 1 2 0 1 0 2 1 . 0.71 24.901 0 2 0 1 0 4 1 . 1.95 23.651 0 2 0 1 0 4 0 1 2.80 18.849 0 1 1 0 0 4 0 1 4.50 19.112 0 2 0 1 0 3 0 0 1.03 17.632 0 3 0 0 1 3 1 1 1.50 9.408 1 2 0 1 0 4 0 1 4.10 10.066 1 2 0 1 0 4 1 1 4.70 20.395 0 2 0 1 0 3 1 . 1.10 11.579 1 3 0 0 1 4 0 1 2.77 16.875 0 1 1 0 0 3 0 . 0.51 14.605 1 2 0 1 0 4 1 1 5.00 1.020 0 3 0 0 1 4 0 0 2.60 10.559 0 4 0 0 0 4 1 . 1.00 26.217 1 1 1 0 0 4 1 0 2.60 20.395 0 2 0 1 0 4 1 . 5.30 7.368 0 2 0 1 0 4 0 0 2.50 1.678 0 1 1 0 0 3 0 1 1.38 24.638 0 1 1 0 0 2 0 . 0.25 19.211 0 3 0 0 1 4 1 . 2.30 23.914 0 2 0 1 0 4 1 0 2.10 24.309 0 1 1 0 0 3 1 0 1.70 1.842 0 2 0 1 0 2 1 . 0.42 11.678 0 3 0 0 1 4 1 . 4.00 18.783 0 2 0 1 0 4 1 1 3.30 19.868 0 1 1 0 0 4 0 1 2.60 9.671 0 2 0 1 0 4 1 1 5.80 16.875 0 2 0 1 0 4 1 1 3.63 15.888 0 1 1 0 0 2 1 1 0.50 8.586 0 1 1 0 0 2 0 0 0.36 26.908 0 1 1 0 0 . 1 1 11.00 22.895 0 2 0 1 0 4 1 1 6.00 2.204 0 1 1 0 0 3 1 . 1.00 1.645 0 2 0 1 0 3 1 0 1.28 12.007 0 2 0 1 0 3 0 0 1.20 11.053 1 1 1 0 0 4 0 0 2.12 10.691 1 1 1 0 0 4 0 0 0.99 13.750 0 1 1 0 0 3 0 0 1.18 12.500 0 2 0 1 0 3 1 0 1.18 130.757 0 2 0 1 0 2 0 0 0.33 22.368 0 2 0 1 0 4 1 0 0.88 15.395 0 2 0 1 0 4 1 0 2.82 13.553 0 1 1 0 0 3 0 0 1.12 63.684 1 2 0 1 0 3 0 0 1.32 21.513 0 3 0 0 1 4 0 0 1.20 16.941 0 3 0 0 1 4 0 1 3.01 25.164 0 2 0 1 0 4 0 0 1.45 19.803 0 1 1 0 0 3 1 . 0.85 13.7500 1 2 0 1 0 4 0 1 2.38 19.1776 0 2 0 1 0 3 0 . 1.04 29.8355 0 1 1 0 0 4 0 . 0.82 34.7039 0 2 0 1 0 2 0 0 0.52 18.6184 0 1 1 0 0 3 0 . 0.90 36.5132 0 1 1 0 0 4 0 0 1.32 4.3750 0 2 0 1 0 4 0 1 3.00 24.8355 0 1 1 0 0 4 1 0 1.15 34.6053 1 2 0 1 0 3 1 . 0.69 14.9671 0 2 0 1 0 3 0 0 1.40 20.7566 0 1 1 0 0 3 1 . 3.08 29.2434 0 2 0 1 0 3 1 . 1.10 16.5789 0 1 1 0 0 4 0 0 1.75 5.1316 0 1 1 0 0 4 1 0 3.20 19.9013 0 1 1 0 0 4 1 0 1.72 19.8355 0 1 1 0 0 3 1 1 1.37 0.7237 1 1 1 0 0 . 0 1 3.75 31.1184 0 1 1 0 0 4 0 . 1.62 0.8224 0 1 1 0 0 2 1 0 0.32 1.0526 0 1 1 0 0 . 1 0 3.80 63.4211 1 2 0 1 0 2 1 . 0.24 14.0789 1 2 0 1 0 4 1 0 3.28 7.2697 0 1 1 0 0 4 0 1 2.60 20.5921 0 2 0 1 0 4 0 . 3.40 25.8224 0 2 0 1 0 3 0 0 0.45 1.1184 0 1 1 0 0 4 1 1 1.05 1.9737 0 1 1 0 0 3 1 0 0.75 12.6974 1 3 0 0 1 2 1 1 0.50 18.6513 0 1 1 0 0 4 1 1 2.50 16.0197 0 2 0 1 0 3 0 . 1.00 5.7237 1 1 1 0 0 2 0 1 0.35 9.7368 0 2 0 1 0 3 1 0 3.50 2.6316 1 4 0 0 0 4 1 1 3.10 85.7237 1 1 1 0 0 4 1 0 1.75 13.0263 0 3 0 0 1 3 1 . 1.00 22.1053 0 2 0 1 0 4 1 . 1.93 77.8618 1 1 1 0 0 4 0 0 2.30 17.993 0 2 0 1 0 4 1 . 2.92 19.803 0 2 0 1 0 3 1 1 0.70 0.592 0 1 1 0 0 4 0 . 2.80 -130.164 1 2 0 1 0 3 1 . 0.72 9.868 1 1 1 0 0 4 1 0 2.40 1.316 0 2 0 1 0 4 1 0 2.10 19.145 0 2 0 1 0 2 1 0 0.65 13.816 0 2 0 1 0 4 1 . 1.20 62.368 1 . . . 4 0 . 1.10 63.355 1 2 0 1 0 3 1 1 2.40 9.013 0 1 1 0 0 3 1 1 1.40 1.842 0 1 1 0 0 4 1 . 3.70 18.849 0 3 0 0 1 3 1 . 1.40 11.974 0 2 0 1 0 3 1 0 0.89 7.895 1 2 0 1 0 4 0 1 6.70 4.211 0 2 0 1 0 2 0 0 0.23 4.836 0 2 0 1 0 3 1 . 0.90 0.789 0 3 0 0 1 2 1 . 0.54 14.309 0 2 0 1 0 2 1 0 0.51 21.250 0 2 0 1 0 4 0 . 3.14 17.072 0 3 0 0 1 2 1 0 0.49 17.763 0 1 1 0 0 2 1 0 0.85 0.428 0 1 1 0 0 4 0 0 3.30 178.947 1 3 0 0 1 3 1 0 0.40 14.309 0 1 1 0 0 4 0 0 1.89 17.862 0 1 1 0 0 3 1 1 0.57 17.763 0 2 0 1 0 4 1 0 2.25 26.875 0 2 0 1 0 3 1 1 1.60 17.961 0 2 0 1 0 2 0 0 0.23 13.750 1 2 0 1 0 4 1 0 3.00 11.908 0 1 1 0 0 3 0 0 0.35 57.204 1 3 0 0 1 3 0 . 1.50 0.461 0 . . . 4 1 1 2.23 20.230 0 1 1 0 0 2 0 0 2.10 15.921 0 2 0 1 0 3 0 0 1.00 39.934 1 1 1 0 0 4 1 0 1.62 19.408 0 2 0 1 0 . 0 . 2.30 13.4868 0 1 1 0 0 3 1 0 3.41 4.5066 0 1 1 0 0 3 1 0 1.05 1.5461 0 1 1 0 0 4 1 0 1.55 23.2237 0 2 0 1 0 4 1 . 1.00 6.6776 0 1 1 0 0 3 1 0 1.05 9.1118 0 2 0 1 0 4 1 0 1.76 12.4342 1 2 0 1 0 4 1 . 6.90 12.0066 1 1 1 0 0 4 1 0 2.46 0.4605 0 3 0 0 1 4 1 . 0.72 3.2566 0 2 0 1 0 2 0 0 0.45 16.0526 0 1 1 0 0 3 0 0 0.83 18.7829 0 2 0 1 0 2 0 0 0.26 32.8618 1 2 0 1 0 3 1 0 1.33 12.5987 0 2 0 1 0 2 0 0 0.53 31.2829 0 1 1 0 0 4 0 0 2.70 4.3421 0 2 0 1 0 3 0 0 0.92 18.9803 0 2 0 1 0 4 1 1 10.00 13.2895 0 3 0 0 1 . 0 0 5.60 17.0724 0 1 1 0 0 4 0 0 1.00 4.4079 0 1 1 0 0 4 0 . 2.92 13.1250 0 3 0 0 1 4 1 1 3.25 16.4803 0 1 1 0 0 4 1 1 4.91 18.9145 0 1 1 0 0 4 1 1 1.50 0.3947 0 2 0 1 0 2 1 1 0.30 20.0987 0 3 0 0 1 4 1 0 2.27 19.0461 1 1 1 0 0 4 0 0 1.65 1.6776 0 2 0 1 0 . 1 1 6.87 13.2566 0 3 0 0 1 4 1 0 2.20 24.3092 0 2 0 1 0 3 0 0 1.80 16.0197 0 1 1 0 0 3 0 0 0.80 2.7303 0 1 1 0 0 4 1 0 0.85 5.4605 0 3 0 0 1 4 1 0 1.85 5.3289 1 1 1 0 0 4 0 1 1.00 14.0461 0 3 0 0 1 4 0 1 2.30 14.7368 0 1 1 0 0 4 1 0 2.50 22.7303 0 1 1 0 0 4 1 . 1.80 16.3816 0 3 0 0 1 4 1 0 1.15 1.184 0 2 0 1 0 4 1 . 1.30 14.507 0 3 0 0 1 4 0 1 2.50 7.862 1 2 0 1 0 4 0 1 4.13 5.099 0 2 0 1 0 4 1 1 3.21 10.000 0 1 1 0 0 4 0 1 1.60 15.362 0 1 1 0 0 4 1 . 2.80 0.461 0 2 0 1 0 2 1 0 0.35 15.789 0 1 1 0 0 4 1 0 1.10 15.855 0 2 0 1 0 4 1 1 2.74 10.592 0 1 1 0 0 4 0 0 2.05 0.000 0 1 1 0 0 2 0 . 0.55 53.618 1 2 0 1 0 4 1 . 7.05 65.230 0 4 0 0 0 4 1 . 5.80 12.697 0 2 0 1 0 2 1 0 0.42 2.303 0 3 0 0 1 4 1 0 4.87 3.947 0 2 0 1 0 2 1 0 0.20 17.072 0 1 1 0 0 4 0 1 3.64 14.112 0 3 0 0 1 . 1 1 4.80 0.493 0 2 0 1 0 3 0 0 2.00 162.138 1 1 1 0 0 4 1 0 1.58 12.993 0 2 0 1 0 3 1 1 3.00 12.763 0 2 0 1 0 2 1 . 0.42 15.724 0 2 0 1 0 4 0 1 1.00 19.605 1 1 1 0 0 4 1 1 2.75 15.691 0 1 1 0 0 4 1 0 1.50 15.033 0 1 1 0 0 4 1 0 1.10 17.303 0 3 0 0 1 . 1 . 3.40 0.724 0 3 0 0 1 . 0 1 13.00 0.691 1 3 0 0 1 4 1 . 2.55 20.757 0 2 0 1 0 4 0 . 2.80 13.355 0 2 0 1 0 4 1 0 1.30 4.211 0 1 1 0 0 4 0 0 1.45 15.164 0 3 0 0 1 4 0 0 1.68 0.526 0 3 0 0 1 4 0 . 1.20 0.395 0 1 1 0 0 3 0 . 2.50 10.855 0 3 0 0 1 4 0 1 5.00 15.691 0 2 0 1 0 4 1 1 3.86 22.3684 1 1 1 0 0 4 0 . 4.00 61.1513 0 2 0 1 0 2 0 0 0.50 13.0263 0 3 0 0 1 4 0 0 2.60 15.4276 0 1 1 0 0 4 1 . 1.08 15.8553 0 2 0 1 0 3 1 . 0.75 14.7697 0 1 1 0 0 4 1 0 2.45 15.7895 0 2 0 1 0 4 0 1 1.75 8.7171 0 1 1 0 0 2 0 . 0.63 15.3947 0 2 0 1 0 4 0 1 2.32 13.4868 0 1 1 0 0 4 0 . 0.82 12.6974 0 1 1 0 0 4 0 1 3.35 14.9342 0 1 1 0 0 4 0 0 1.87 6.6118 1 1 1 0 0 4 1 . 0.80 14.6711 0 2 0 1 0 . 0 1 3.13 12.7632 0 3 0 0 1 2 0 . 0.40 15.3947 0 1 1 0 0 3 0 0 1.04 8.0592 0 2 0 1 0 2 0 . 0.52 13.6842 0 1 1 0 0 3 0 0 0.43 9.9671 0 1 1 0 0 4 0 0 1.00 5.0329 0 3 0 0 1 1 1 . 0.30 13.2895 0 3 0 0 1 4 1 1 1.04 11.6447 0 2 0 1 0 3 1 0 0.58 20.8882 0 2 0 1 0 4 1 1 2.57 2.6316 0 2 0 1 0 3 0 0 0.98 5.5592 0 3 0 0 1 3 0 . 1.26 26.8421 0 1 1 0 0 2 1 1 0.30 11.2829 0 1 1 0 0 4 0 0 1.72 7.9276 0 1 1 0 0 4 0 1 2.35 11.3816 0 2 0 1 0 2 0 1 0.40 13.2566 0 1 1 0 0 2 0 0 0.40 12.9605 0 2 0 1 0 3 1 . 1.29 11.2171 0 2 0 1 0 4 1 0 6.00 3.2895 0 2 0 1 0 4 1 0 1.05 0.7566 0 2 0 1 0 4 0 0 1.40 7.0066 1 1 1 0 0 3 1 1 1.04 22.6316 1 1 1 0 0 2 1 0 0.68 12.5329 0 2 0 1 0 4 1 . 4.50 13.3553 0 2 0 1 0 4 1 0 3.40 13.4539 0 2 0 1 0 4 1 0 1.50 45.0000 0 1 1 0 0 2 0 0 0.40 0.3618 0 3 0 0 1 2 1 . 0.60 12.4671 0 3 0 0 1 2 0 1 0.50 29.9342 1 2 0 1 0 1 0 0 0.47 14.3092 0 1 1 0 0 4 0 1 2.50 0.9211 0 3 0 0 1 4 1 1 3.00 1.3158 1 1 1 0 0 4 1 0 10.20 1.6118 0 1 1 0 0 4 0 0 2.10 0.4934 0 1 1 0 0 2 0 0 0.30 12.2368 0 1 1 0 0 4 0 0 1.75 14.1447 0 2 0 1 0 2 1 0 0.30 5.0658 0 2 0 1 0 3 1 0 1.10 12.6316 0 1 1 0 0 4 1 1 3.10 8.4211 0 3 0 0 1 4 1 . 3.70 10.3618 0 2 0 1 0 4 1 1 2.25 5.5592 0 2 0 1 0 4 1 1 2.75 6.6447 1 2 0 1 0 4 0 0 5.30 13.2566 0 3 0 0 1 . 1 0 2.25 10.9539 0 1 1 0 0 4 1 0 2.00 12.7961 0 2 0 1 0 3 1 . 0.77 14.4079 0 1 1 0 0 3 0 1 0.85 18.4211 0 2 0 1 0 3 1 0 1.40 22.3684 0 1 1 0 0 3 0 0 1.00 5.0329 0 2 0 1 0 4 1 1 2.35 5.9868 0 3 0 0 1 2 1 0 0.50 1.6118 0 2 0 1 0 3 0 0 1.20 11.8421 0 2 0 1 0 4 1 0 1.30 10.0658 0 1 1 0 0 4 0 1 5.00 40.9211 1 2 0 1 0 4 1 1 2.34 9.9671 0 2 0 1 0 4 0 1 1.50 93.9803 1 1 1 0 0 3 0 . 8.00 31.0526 1 2 0 1 0 4 1 . 1.75 2.2039 0 2 0 1 0 2 0 0 0.23 12.1711 0 2 0 1 0 3 1 0 1.00 2.4013 0 2 0 1 0 4 1 0 2.16 11.382 0 2 0 1 0 3 0 0 1.01 12.993 0 1 1 0 0 4 0 1 3.40 0.954 0 2 0 1 0 4 1 1 0.85 0.724 0 1 1 0 0 4 0 1 4.50 10.296 0 3 0 0 1 4 1 1 1.87 5.362 0 1 1 0 0 4 0 0 2.30 12.434 0 1 1 0 0 4 0 1 2.10 12.862 0 3 0 0 1 2 1 0 0.40 2.961 0 2 0 1 0 4 1 1 1.60 11.776 0 3 0 0 1 3 1 1 4.20 67.072 0 3 0 0 1 3 1 . 1.00 7.993 1 2 0 1 0 4 1 0 1.72 13.191 1 1 1 0 0 4 0 1 4.00 10.099 1 1 1 0 0 4 1 1 1.60 11.743 0 2 0 1 0 3 1 0 0.64 0.099 1 2 0 1 0 4 0 0 2.45 0.789 0 1 1 0 0 3 0 . 0.55 5.954 0 3 0 0 1 2 1 0 0.40 9.243 0 2 0 1 0 4 1 0 1.20 57.368 1 1 1 0 0 3 1 . 0.94 1.184 0 2 0 1 0 2 0 0 0.32 18.717 1 3 0 0 1 3 1 0 0.86 11.875 0 3 0 0 1 2 0 . 0.65 11.316 0 2 0 1 0 3 1 0 2.25 15.230 1 1 1 0 0 4 0 . 1.50 56.020 0 2 0 1 0 4 1 0 5.50 5.757 0 3 0 0 1 2 0 0 0.30 1.743 1 2 0 1 0 . 1 . 1.40 10.855 0 1 1 0 0 4 0 1 3.87 7.039 0 2 0 1 0 3 1 1 1.10 7.829 0 1 1 0 0 2 0 0 0.40 1.875 0 1 1 0 0 3 1 . 0.80 15.428 0 1 1 0 0 3 0 0 1.80 146.941 0 2 0 1 0 2 0 0 0.34 81.776 1 3 0 0 1 2 0 0 0.80 9.605 0 1 1 0 0 3 0 0 0.60 10.033 0 1 1 0 0 3 0 0 0.57 12.1711 0 1 1 0 0 . 0 0 2.87 4.3421 0 1 1 0 0 2 0 0 0.40 5.1974 1 4 0 0 0 . 0 1 8.00 7.5987 0 3 0 0 1 3 0 0 0.65 8.8158 0 2 0 1 0 3 1 1 0.80 36.2500 1 1 1 0 0 3 1 . 4.00 9.2105 0 3 0 0 1 3 0 0 1.04 6.7763 0 2 0 1 0 2 1 0 0.30 6.5461 0 1 1 0 0 3 1 0 3.63 11.0526 0 1 1 0 0 4 0 0 2.20 11.1184 0 2 0 1 0 4 0 . 1.62 11.5461 0 1 1 0 0 3 0 0 0.60 2.2697 0 1 1 0 0 4 0 1 1.00 40.5921 1 1 1 0 0 4 0 0 0.63 3.0263 0 2 0 1 0 4 1 0 3.37 13.9474 0 2 0 1 0 2 0 . 0.38 1.1513 0 1 1 0 0 3 0 0 1.00 13.0263 0 2 0 1 0 4 0 0 1.50 1.7105 0 4 0 0 0 3 1 0 1.20 1.7763 0 3 0 0 1 4 1 0 1.42 3.1250 0 1 1 0 0 4 0 0 1.70 26.4803 1 1 1 0 0 4 0 0 1.10 2.4671 0 3 0 0 1 4 0 1 5.75 9.1776 0 3 0 0 1 . 1 1 4.10 4.6711 0 1 1 0 0 4 0 0 2.60 5.9211 0 3 0 0 1 4 1 1 5.12 71.4145 1 1 1 0 0 4 1 1 2.50 3.0592 0 1 1 0 0 4 1 0 2.00 6.6776 0 1 1 0 0 2 0 0 0.24 10.4934 0 2 0 1 0 3 0 0 1.35 4.0461 0 3 0 0 1 3 1 0 0.32 3.0921 0 1 1 0 0 2 1 . 0.61 8.0592 0 1 1 0 0 3 1 0 0.59 51.7434 1 2 0 1 0 4 0 1 2.50 2.0066 0 1 1 0 0 3 0 . 1.45 9.6053 0 1 1 0 0 4 1 1 1.30 7.6645 0 1 1 0 0 3 1 . 1.50 0.625 1 1 1 0 0 4 0 . 1.80 1.151 0 1 1 0 0 2 0 . 0.40 10.592 0 1 1 0 0 4 1 0 4.55 0.329 0 1 1 0 0 4 0 1 7.25 129.605 1 1 1 0 0 3 1 . 2.50 1.875 0 2 0 1 0 3 1 0 1.00 11.316 0 2 0 1 0 4 1 0 1.59 6.711 0 2 0 1 0 2 0 0 0.40 4.934 0 3 0 0 1 4 1 0 1.51 11.480 0 1 1 0 0 . 0 0 8.00 11.941 0 2 0 1 0 3 1 0 0.85 7.467 0 1 1 0 0 4 0 1 1.85 9.243 0 3 0 0 1 4 0 0 1.30 7.138 0 1 1 0 0 2 0 0 0.78 10.987 0 1 1 0 0 3 1 0 0.50 29.046 1 2 0 1 0 2 1 0 0.28 3.882 0 1 1 0 0 . 0 1 2.50 6.020 0 3 0 0 1 . 1 0 4.00 0.789 0 1 1 0 0 4 0 0 1.50 20.592 0 2 0 1 0 . 1 0 4.50 9.375 0 2 0 1 0 4 1 0 1.42 9.967 0 1 1 0 0 3 0 0 1.30 1.217 0 2 0 1 0 2 1 0 0.65 9.375 0 1 1 0 0 4 1 1 1.92 10.362 0 2 0 1 0 4 0 1 3.00 7.895 0 1 1 0 0 4 1 . 0.80 5.724 0 2 0 1 0 3 1 0 0.82 1.184 0 2 0 1 0 4 1 1 4.60 7.796 0 1 1 0 0 3 0 1 3.70 10.362 0 3 0 0 1 4 1 1 2.89 9.441 0 1 1 0 0 4 0 1 4.00 1.151 0 2 0 1 0 4 1 1 6.20 7.961 0 2 0 1 0 4 1 0 0.90 8.717 0 3 0 0 1 4 1 1 5.00 6.908 0 2 0 1 0 2 0 0 0.34 4.671 0 1 1 0 0 3 1 0 1.35 7.401 0 1 1 0 0 2 0 0 0.40 10.461 1 2 0 1 0 4 0 . 7.40 7.270 0 2 0 1 0 4 1 0 0.95 1.414 0 3 0 0 1 4 0 0 1.68 1.086 0 3 0 0 1 4 1 0 1.95 1.579 0 1 1 0 0 2 0 0 0.70 1.941 0 1 1 0 0 2 0 0 0.33 7.763 0 2 0 1 0 4 1 . 1.90 8.684 0 3 0 0 1 4 1 0 2.80 1.579 0 2 0 1 0 4 1 1 7.00 8.322 0 2 0 1 0 4 1 0 6.00 11.743 0 1 1 0 0 2 0 . 0.30 7.105 0 2 0 1 0 3 0 . 0.80 14.507 1 1 1 0 0 3 0 0 1.25 9.112 1 3 0 0 1 3 1 . 1.60 7.401 0 1 1 0 0 4 1 . 1.40 132.138 1 3 0 0 1 1 0 0 0.30 8.289 0 3 0 0 1 4 1 1 1.14 8.388 0 1 1 0 0 4 0 0 0.95 7.007 0 1 1 0 0 2 1 0 0.61 7.105 0 1 1 0 0 3 0 1 1.10 6.414 0 1 1 0 0 3 1 . 0.51 11.053 1 1 1 0 0 4 0 0 2.20 8.059 0 2 0 1 0 3 1 1 1.95 16.250 1 4 0 0 0 3 1 . 4.00 44.507 1 2 0 1 0 3 0 0 1.00 4.211 0 3 0 0 1 4 0 . 0.92 8.388 0 2 0 1 0 4 1 0 3.12 5.592 0 1 1 0 0 4 1 1 2.75 3.947 0 2 0 1 0 3 1 1 3.40 14.079 0 2 0 1 0 4 1 1 1.39 7.730 0 1 1 0 0 3 0 0 1.22 5.855 1 3 0 0 1 3 0 1 2.80 7.204 0 1 1 0 0 3 1 1 0.80 0.461 0 1 1 0 0 3 0 0 0.60 71.809 1 2 0 1 0 3 1 0 1.01 23.882 1 2 0 1 0 3 1 0 0.65 1.349 0 2 0 1 0 4 0 1 12.60 7.993 0 1 1 0 0 4 0 0 1.50 6.447 0 1 1 0 0 3 0 0 0.76 5.526 0 1 1 0 0 . 0 . 11.00 4.967 0 1 1 0 0 4 1 1 3.50 10.789 0 3 0 0 1 3 1 1 1.50 79.671 1 3 0 0 1 3 1 0 1.25 46.743 0 2 0 1 0 3 1 0 0.60 5.099 0 2 0 1 0 3 1 0 0.82 0.362 0 1 1 0 0 2 1 0 0.80 4.046 0 3 0 0 1 4 1 . 0.50 7.368 0 2 0 1 0 2 1 0 0.74 5.855 1 2 0 1 0 4 1 1 5.03 0.921 0 2 0 1 0 4 1 1 2.00 2.434 0 2 0 1 0 4 0 1 1.30 12.928 1 1 1 0 0 4 1 1 3.50 7.467 0 1 1 0 0 4 0 0 2.03 2.039 0 1 1 0 0 3 0 0 0.80 25.855 1 1 1 0 0 4 0 1 3.75 6.908 0 3 0 0 1 2 0 0 0.20 4.572 0 3 0 0 1 3 0 0 1.50 4.638 0 2 0 1 0 4 0 1 3.60 0.296 0 1 1 0 0 . 0 . 2.25 3.947 0 2 0 1 0 4 1 . 3.00 81.250 0 2 0 1 0 4 1 1 0.90 6.875 0 2 0 1 0 4 1 . 2.50 0.461 0 2 0 1 0 3 0 1 1.60 6.974 0 1 1 0 0 . 0 1 3.15 4.046 0 1 1 0 0 4 0 1 1.70 4.901 0 1 1 0 0 2 1 0 0.20 3.947 0 2 0 1 0 3 1 0 0.60 3.191 0 2 0 1 0 4 1 . 1.25 0.691 0 1 1 0 0 4 0 0 1.85 120.428 1 1 1 0 0 4 1 . 2.15 3.717 0 2 0 1 0 4 1 . 1.40 4.770 0 3 0 0 1 2 1 0 0.25 4.967 0 1 1 0 0 4 0 . 1.65 4.605 0 2 0 1 0 4 0 0 1.05 34.046 0 1 1 0 0 . 1 1 1.90 1.612 0 1 1 0 0 4 0 . 1.40 0.461 0 2 0 1 0 4 1 1 3.20 5.329 0 1 1 0 0 4 0 . 2.25 13.651 0 2 0 1 0 3 1 0 1.56 2.039 0 1 1 0 0 4 0 0 3.10 1.711 0 1 1 0 0 3 1 . 1.05 7.697 1 2 0 1 0 2 1 . 0.45 131.546 1 1 1 0 0 . 0 0 7.68 5.822 0 1 1 0 0 4 0 0 3.00 5.559 0 1 1 0 0 3 1 0 1.10 59.605 1 2 0 1 0 4 1 0 1.35 47.632 0 1 1 0 0 2 1 0 0.18 0.033 0 2 0 1 0 4 1 0 0.75 71.283 0 2 0 1 0 2 1 0 0.26 51.546 0 1 1 0 0 2 1 0 1.00 154.309 0 2 0 1 0 2 0 0 0.70 0.197 0 3 0 0 1 3 1 0 0.35 20.691 0 3 0 0 1 3 1 1 3.50 7.862 1 2 0 1 0 4 1 0 2.50 79.178 0 4 0 0 0 2 1 0 0.35 7.566 0 2 0 1 0 3 0 1 0.59 25.263 0 1 1 0 0 2 1 . 0.30 15.197 0 2 0 1 0 2 1 1 1.10 28.487 0 2 0 1 0 4 1 0 2.50 22.171 0 1 1 0 0 4 1 . 1.25 54.112 0 3 0 0 1 4 1 1 2.30 7.862 1 3 0 0 1 3 1 . 0.90 50.362 0 2 0 1 0 3 1 0 1.00 86.809 0 2 0 1 0 4 1 0 1.31 9.507 0 1 1 0 0 4 1 . 1.00 45.033 1 2 0 1 0 4 1 0 0.95 30.888 0 2 0 1 0 3 1 1 0.70 9.868 0 2 0 1 0 2 1 0 0.45 0.625 0 2 0 1 0 2 1 0 0.18 20.822 0 3 0 0 1 4 1 1 5.00 2.730 0 1 1 0 0 2 0 0 0.32 2.6974 0 2 0 1 0 4 1 0 3.00 6.0855 0 2 0 1 0 4 1 . 2.20 1.8750 0 1 1 0 0 3 1 0 0.60 13.3553 0 3 0 0 1 3 1 0 0.70 19.1776 0 2 0 1 0 4 1 . 1.50 11.1184 1 3 0 0 1 3 1 . 0.75 48.1908 1 2 0 1 0 4 1 0 2.30 3.8487 1 2 0 1 0 3 0 0 0.70 44.8684 0 2 0 1 0 4 1 0 1.50 1.1842 0 2 0 1 0 2 1 0 0.50 2.2039 0 2 0 1 0 3 1 0 0.75 26.6776 0 2 0 1 0 3 1 0 0.75 17.0395 0 1 1 0 0 2 1 0 0.20 9.7697 0 2 0 1 0 4 0 0 2.54 1.8750 0 3 0 0 1 2 1 0 0.73 2.1382 0 1 1 0 0 3 1 0 0.90 3.8158 0 1 1 0 0 3 0 0 0.40 0.6579 0 3 0 0 1 . 1 . 3.50 5.8882 0 2 0 1 0 4 1 1 2.75 8.6842 0 1 1 0 0 2 1 0 0.45 38.0921 0 2 0 1 0 4 1 . 1.70 60.0987 1 1 1 0 0 4 1 0 2.20 9.9342 0 2 0 1 0 2 1 0 0.34 41.5789 1 1 1 0 0 3 1 1 1.80 2.2697 0 1 1 0 0 . 0 0 6.75 10.3618 0 2 0 1 0 4 1 . 1.38 39.2105 1 2 0 1 0 4 1 0 1.56 66.1184 1 2 0 1 0 4 0 0 0.58 2.1711 1 3 0 0 1 4 1 0 3.48 93.3553 1 2 0 1 0 3 1 0 1.38 31.6447 1 1 1 0 0 3 1 0 0.75 5.2632 1 2 0 1 0 . 1 0 6.50 60.0658 1 2 0 1 0 4 0 . 3.40 0.6908 1 3 0 0 1 4 1 0 2.10 50.8224 0 2 0 1 0 3 1 . 0.75 32.5329 1 1 1 0 0 3 1 . 1.10 45.0329 0 1 1 0 0 4 0 1 2.30 9.243 0 2 0 1 0 4 0 0 1.00 44.276 1 2 0 1 0 4 1 . 2.58 5.625 0 3 0 0 1 4 1 1 3.50 5.987 0 3 0 0 1 4 0 1 4.50 6.283 0 1 1 0 0 . 0 0 3.00 10.921 0 1 1 0 0 4 0 1 2.10 5.329 0 1 1 0 0 4 1 1 3.30 5.164 0 1 1 0 0 4 0 . 1.15 10.099 1 2 0 1 0 4 1 . 1.20 10.066 0 2 0 1 0 4 1 0 2.70 4.408 0 2 0 1 0 4 1 0 1.30 43.980 1 2 0 1 0 3 0 0 1.60 16.217 0 4 0 0 0 4 0 1 8.10 16.711 1 1 1 0 0 . 1 . 5.70 5.592 0 2 0 1 0 4 1 . 2.89 6.447 0 2 0 1 0 4 1 1 2.24 5.592 0 3 0 0 1 3 1 . 1.47 14.112 1 2 0 1 0 4 1 . 4.00 4.901 0 1 1 0 0 4 0 . 1.10 11.020 0 3 0 0 1 4 1 1 1.43 5.362 0 3 0 0 1 4 1 0 1.49 11.842 1 1 1 0 0 4 1 1 1.96 4.704 0 2 0 1 0 4 1 1 1.10 235.559 1 2 0 1 0 3 1 0 2.24 5.329 0 3 0 0 1 . 1 1 4.18 46.118 0 3 0 0 1 4 0 . 1.10 4.013 0 1 1 0 0 4 1 1 6.50 ; **************************************************** /* The following data set is discussed on page 45 ,*/ /* where it is used to produce output 2.8. */ **************************************************** data; input l r; datalines; 0 0.0493 0 0.2849 0 0.4082 0.0517 0.8767 0.0627 0.8877 0.2983 1.1233 1.2247 15 0.5503 1.3753 0.7175 1.5425 0.7586 1.5836 0.9147 1.7397 0.9339 1.7589 0.9476 1.7726 1.0983 1.9233 1.9562 15 1.2243 2.0493 1.4736 2.2986 1.5175 2.3425 2.9065 3.7315 3.2298 4.0548 4.0685 15 3.7613 4.5863 4.1284 4.9534 5.1534 15 5.7315 15 5.0243 5.8493 5.0435 5.8685 6.0712 15 6.1151 15 7.3781 15 7.663 15 8.0438 15 8.189 15 8.2055 15 8.2548 15 8.4274 15 8.4521 15 8.7589 15 9.0356 15 9.8959 15 9.9151 15 9.9178 15 10.1151 15 10.4027 15 10.6 15 9.8353 10.6603 10.6685 15 10.726 15 10.926 15 10.937 15 11.2027 15 11.4548 15 11.4712 15 11.5589 15 11.6082 15 11.6164 15 11.6521 15 11.7123 15 11.7671 15 11.8466 15 11.8575 15 11.8685 15 11.9863 15 12.0082 15 ; ************************************************************************* ************************************************************************* EXAMPLE CODE USED IN THIS BOOK ****************************************************** /*The following example code appears on page 29. */ /*It produced output 2.1. */ ****************************************************** data; input time d @@; cards; 0.0493 1 0.2849 1 0.4082 1 0.8767 1 0.8877 1 1.1233 1 1.2247 0 1.3753 1 1.5425 1 1.5836 1 1.7397 1 1.7589 1 1.7726 1 1.9233 1 1.9562 0 2.0493 1 2.2986 1 2.3425 1 3.7315 1 4.0548 1 4.0685 0 4.5863 1 4.9534 1 5.1534 0 5.7315 0 5.8493 1 5.8685 1 6.0712 0 6.1151 0 7.3781 0 7.6630 0 8.0438 0 8.1890 0 8.2055 0 8.2548 0 8.4274 0 8.4521 0 8.7589 0 9.0356 0 9.8959 0 9.9151 0 9.9178 0 10.1151 0 10.4027 0 10.6000 0 10.6603 1 10.6685 0 10.7260 0 10.9260 0 10.9370 0 11.2027 0 11.4548 0 11.4712 0 11.5589 0 11.6082 0 11.6164 0 11.6521 0 11.7123 0 11.7671 0 11.8466 0 11.8575 0 11.8685 0 11.9863 0 12.0082 0 ; proc lifetest plots=(s); time time*d(0); run; ****************************************************** /*The following example code appears on page 33. */ /*It produced output 2.2. */ ****************************************************** %kmtable( dataset= leuk /* default is _last_ */ /* default is 95 for 95% CI */ ,time= time /* time variable */ ,cens=d /* variable indicating censored or complete times */ ,censval= 0 /* value(s) that indicate censored observation */ ,method= 2 /* 1 for method used in Proc Lifetest 2 for method that yields limits in (0,1) */ ,variance=p /* G or g for Greenwood's formula, P or p for Peto's formula */ /* optional variable for separate tables */ /* yes (default) to print table, no to suppress printing */ ) /* or equivalently */ %kmtable(dataset=leuk, time=time, cens=d, censval=0, method=2 variance=p) ****************************************************** /*The following example code appears on page 36 and */ /*and produces figure 2.3. */ ****************************************************** %kmplot( mark= yes /* yes to mark times on curve, no (default) to not mark times */ ,ci= yes /* yes for confidence intervals, no (default) for no ci's */ /* yes to produce multiple plots on the same graph, no (default) for separate plots */ /* label for the horizontal axis, default is time */ /* label for the vertical axis, default is Pct Survival */ ,title= Figure 2.3 /* default is Kaplan-Meier Survival Curve */ /* clause to restrict curve(s), usually of form time < value or r < value (for n at risk)*/ ) ****************************************************** /*The following example code appears on page 40 and */ /*and produced output 2.5. */ ****************************************************** %macro doit; %do nn = 120 %to 260 %by 20; title "Sample Size = &nn"; %kmplan(s = exp(-.045*u), h = .045 ,N = &nn , A = 4 , tau = 3 , t = 5); %end; %mend; %doit; ****************************************************** /*The following example code appears on page 44 */ /*and produced output 2.6. */ ****************************************************** data ex1; input l r f; cards; 0 2 1 1 3 1 2 10 4 4 10 4 ; run; %ice(data=ex1, time=(l r), freq=f, options=plot) *************************************************** /* The following code is found on page 60. It */ /* produced output 3.1. */ ************************************************** data; set tmp1.breastdata; if agegrp = '0-59' then age = 'LT 60'; else age = 'GE 60'; run; symbol1 c=black l = 1 w = 3; symbol2 c=black l = 3 w = 3; proc lifetest censoredsymbol = none plots = (s) timelist = 1 to 8; time years*cens(0); strata age; run; *************************************************** /* The following code is found on page 67. It */ /* produced output 3.2. */ ************************************************** data x; set breastdata; if agegrp = '0-59' then age = 'LT 60'; else age = 'GE 60'; %include 'c:\linrank.sas'; %linrank( time=years, cens= cens , censval=0 ,groupvar= age ) *************************************************** /* The following code is found on page 77. It */ /* produced output 3.6. */ ************************************************** data x; input time cens group; cards; 3 1 1 5 1 1 8 0 1 10 1 1 15 1 1 2 1 2 5 1 2 11 0 2 13 0 2 14 1 2 16 1 2 ; %perm_gen( indata = x, time = time, cens = cens, n1 = 5, n2 = 6, group = group); %test(time = time , cens = cens, censval = 0 , test = logrank, group = group, type = perm); *************************************************** /* The following code is found on page 81. It */ /* produced output 3.8. */ ************************************************** data; input patid state time; cards; 1 1 0 1 2 3.1 1 3 5.0 2 2 0 2 1 3.2 2 0 5.0 3 1 0 3 2 4.2 3 3 5.1 ; %mantbyar(dataset= _last_ , state=state , time=time, nstates=2 , format= default.,id=patid); *************************************************** /* The following code is found on pages 88-89. It*/ /* produced output 3.10. */ ************************************************** data group1; input t s; cards; 0 1.00 1 0.86 2 0.68 3 0.60 4 0.51 5 0.39 6 0.25 7 0.24 8 0.23 ; data group2; input t s; cards; 0 1.00 1 0.95 2 0.85 3 0.75 4 0.65 7 0.25 8 0.24 ; %survpow(s1=group1 ,s2=group2 /* data sets that define the alternative survival distributions for groups 1 and 2*/ ,actime=4 /* accrual time */ ,futime=3 /* post-accrual followup time */ ,rate=30 /* accrual rate */ ) ; *************************************************** /* The following code is found on pages 89-90. It*/ /* produced output 3.11. */ ************************************************** /* Create data set for weight functions. n**.5 for the Tarone Ware statistic, n for the Gehan statistic, and 1 for the log rank statistic */ data data; input w $; cards; (n**.5) n 1 ; /* Create macro variables from weight functions */ data _null_; set data; i=_n_; call symput('w'||left(i), w); run; /* Create data sets for alternative */ data group1; input t s; cards; 0 1.00 1 0.86 2 0.68 3 0.60 4 0.51 5 0.39 6 0.25 7 0.24 8 0.23 ; data group2; input t s; cards; 0 1.00 1 0.95 2 0.85 3 0.75 4 0.65 7 0.25 8 0.24 ; /* loop on accrual rates and weight functions */ %macro loop; %do arate=40 %to 70 %by 5; %do jj=1 %to 3; %survpow(s1=group1, s2= group2, actime=4,futime=3 ,rate=&arate, w=&&w&jj ) ; %end; %end; %mend; %loop; run; *************************************************** /* The following code is found on pages 118. It */ /* produced output 4.1. */ *************************************************** data; set sas.melanoma; if clarklevel = 'I' then clark = 1; if clarklevel = 'II' then clark = 2; if clarklevel = 'III' then clark = 3; if clarklevel = 'IV' then clark = 4; if clarklevel = 'V' then clark = 5; months = (followup - biopsy)/30.4; if ulceration = 'Yes' then ulcer = 1; if ulceration = 'No' then ulcer = 0; if status = 'Dead' then d = 1; else d = 0; proc phreg; model months*d(0) = clark; proc phreg; model months*d(0) = ulcer; proc phreg; model months*d(0) = thickness; run; *************************************************** /* The following code is found on pages 122. It */ /* produced output 4.3. */ *************************************************** data; set sas.melanoma; months = (followup - biopsy)/30.4; if ulceration = 'Yes' then ulcer = 1; if ulceration = 'No' then ulcer = 0; if status = 'Dead' then d = 1; else d = 0; thicknessxulcer = thickness*ulcer; proc phreg; model months*d(0) = ulcer thickness thicknessxulcer; run; ************************************************** /*The following example code appears on page 127.*/ /*It produced output 4.5. */ ************************************************** proc phreg; model dfstime*dfscens(0) = loc1-loc3 sex clark thickness ulcer/ selection = forward rl details; title1 'Proportional Hazards Regresion Analysis'; title2 'Of Melanoma Data'; run; ************************************************** /*The following is referred to on page 135. It */ /*was written by Leslie Kalish who has graciously*/ /*allowed me to use it in my book and include it */ /*in this site. */ ************************************************** * tvcov.mac Macro for implementing time varying covariates in Proc Phreg; /* Author: Leslie A. Kalish New England Research Institutes Email: LesK@neri.org Date: 24 MAY, 2000 Note: This macro has not been extensively tested, although I believe it does work as advertised. Please let me know if you have problems with it. ************************************************************************** INTRODUCTION Normally, programming statements in Proc Phreg are required to implement time-varying covariates. The macro is meant to be placed after the Model statement, replacing the programming statements. Each use of the macro creates one variable, which ordinarily is a covariate that also appears in the model statement. The macro can be used more than once in the same Proc, to create several different covariates. **************************************************************************; USAGE %MACRO TVCOV(HX=,TIME=,OUTVAR=,XTIMES=,COVARS=,SINCE=,LAG=); HX = One of six options for summarizing covariate history. 1, most recent (default) 2, average 3, least squares slope 4, maximum value 5, minimum value 6, range TIME = Time-to-event variable. Must be the same as the outcome variable on the left hand side of the model statement. OUTVAR = Name of covariate created by the macro. Ordinarily, this variable is also on the right hand side of the model statement. XTIMES = Variables holding covariate measurement times. Must be in same units as TIME. COVARS = Variables holding covariate measurement values SINCE = Most distant time to be considered (optional). If omitted, the macro assumes no restriction, i.e., that covariates measured since time 0 are considered. LAG = Most recent time to be considered (optional). If omitted, the macro assumes no restriction, i.e., that covariates up to the present are considered. ======================================================================== EXAMPLE Suppose DEATHT = time from study entry to death (months) DEATH = death censoring indicator (1=dead, 0=alive) X1-X25 = covariate values measured at times T1-T25 T1-T25 = times (must be in same scale as DEATHT) that covariates X1-X25 are measured. To fit a proportional hazards model with the most recently measured covariate as the only covariate, use the following syntax. PROC PHREG; MODEL DEATHT*DEATH(0)=RECENT; %TVCOV(HX=1,TIME=DEATHT,OUTVAR=RECENT,XTIMES=T1-T25,COVARS=X1-X25) RUN; To fit a proportional hazards model with the slope over the past 6 months as the only covariate, use the following syntax. PROC PHREG; MODEL DEATHT*DEATH(0)=SLOPE; %TVCOV(HX=3,TIME=DEATHT,OUTVAR=SLOPE ,XTIMES=T1-T25,COVARS=X1-X25,SINCE=6) RUN; One can include both the most recent value and the slope over the past 6 months in the same model, as follows: PROC PHREG; MODEL DEATHT*DEATH(0)=RECENT SLOPE; %TVCOV(HX=1,TIME=DEATHT,OUTVAR=RECENT,XTIMES=T1-T25,COVARS=X1-X25) %TVCOV(HX=3,TIME=DEATHT,OUTVAR=SLOPE ,XTIMES=T1-T25,COVARS=X1-X25,SINCE=6) RUN; */ * ========================================================================; * MACRO; %macro tvcov(hx=1,time=,outvar=,xtimes=,covars=,since=0,lag=0); %* Trick so that can run macro >1 time in same SAS session.; %global _zz; %if (%length(&_zz) = 0) %then %let _zz = 0; %else %let _zz = %eval(&_zz+1); %* Put measurement values and times in arrays; array _xx&_zz{*} &covars; array _tt&_zz{*} &xtimes; %* Initialize; &outvar=.; %if (&hx=2 | &hx=3 | &hx=6) %then %str(nsofar=0;); %if &hx=2 %then %str(&outvar=0;); %if &hx=3 %then %str(st=.; s_tt=.; sy=.; syt=.;); %if &hx=6 %then %str(mmin=.; mmax=.;); *% Start loop through repeated measurements of covariate; do j=1 to dim(_tt&_zz); %* Cross-product (only needed for slope); %if &hx = 3 %then %str( if (_xx&_zz{j}>. and _tt&_zz{j}>.) then yt = _xx&_zz{j}*_tt&_zz{j}; else yt=.; ); %* Check that time is in correct interval and covar value is nonmissing; %if &since=0 %then %str(if( .< _tt&_zz{j}<=&time-&lag & _xx&_zz{j}>.) then do;); %if &since>0 %then %str(if(&time-&since< _tt&_zz{j}<=&time-&lag & _xx&_zz{j}>.) then do;); %* Evaluate covariate history here; %if &hx = 1 %then %str( &outvar = _xx&_zz{j}; ); %if &hx = 2 %then %str( nsofar=nsofar+1; &outvar = (1/nsofar)*_xx&_zz{j}+(1-1/nsofar)*&outvar; ); %if &hx = 3 %then %str( nsofar=nsofar+1; st=sum(st,_tt&_zz{j}); s_tt=sum(s_tt,_tt&_zz{j}*_tt&_zz{j}); sy=sum(sy,_xx&_zz{j}); syt=sum(syt,yt); if (nsofar > 1) then &outvar=(syt-sy*st/nsofar) / (s_tt-st*st/nsofar); ); %if &hx = 4 %then %str( &outvar=max(_xx&_zz{j},&outvar); ); %if &hx = 5 %then %str( &outvar=min(_xx&_zz{j},&outvar); ); %if &hx = 6 %then %str( nsofar=nsofar+1; mmin=min(_xx&_zz{j},mmin); mmax=max(_xx&_zz{j},mmax); ); end; end; %* Only calculate average if at least one data point; %if &hx = 2 %then %str( if (nsofar = 0) then &outvar = .; ); %* Only calculate range if at least two data points. ; %if &hx = 6 %then %str( if (nsofar > 1) then &outvar = mmax-mmin; ); %mend tvcov; **************************************************** /* The following code appears on page 139. It */ /* produced output 4.7. */ **************************************************** data cov_vals; input thickness ulcer; cards; 4.0 1 .3 0 4.0 0 ; proc phreg data=melanoma noprint; model months*d(0)= thickness ulcer; baseline covariates=cov_vals survival=survival stderr=se lower=lo_bound upper=up_bound out=estsurv / nomean; proc print; run; ******************************************************* /* The following code appears on page 141 - 142. It */ /* produced figure 4.2. */ ******************************************************* proc format; value c 1 = 'Low' 2 = 'Intermediate' 3 = 'High'; proc sort data = estsurv; by thickness ulcer; data estsurv; set estsurv; by thickness ulcer months; retain c 0; if first.ulcer then c+1; format c c. ; label c = 'Risk'; %phplot( yvar=survival /*Variable name associated with keyword survival of baseline statement */ ,ylabel=Pct Survival /*Label for vertical axis. Default is Pct Survival */ ,byvar=c /*Variable used to distinguish covariate vectors */ ,xvar=months /*The time variable used in the proc statement. The default is time */ ,xlabel=Months /*The label for the horizontal axis. The default is Time */ ,combine = yes /*yes to combine curves on one graph, no (the default) to produce separate graphs for each curve. If curves are combined on one graph, confidence intervals can not be plotted. */ ,title = Figure 4.2 /*Title to be printed. Default is Proportional Hazards Survival Curve */ ,lcl = lo_bound /*Variable name associated with lower keyword in baseline statement. Default is lcl */ ,ucl = up_bound /*Variable name associated with upper keyword in baseline statement. Default is ucl */ ) ************************************************** /*The following example code appears on page 149.*/ /*It produced output 4.10. */ ************************************************** proc mi data = dataset out = imputations noprint; var x1 x2; run; proc phreg outest = out covout noprint; model time*cens(0) = x1 x2; by _imputation_; proc mianalyze data = out; var x1 x2; run; ****************************************************** /* The following data set is used for exercise 4.18.2 */ /*on page 150. */ /******************************************************/ /* x1 and x2 are covariates, time is the time the patient was observed. Cens = 1 if the patient died, 0 if censored. */ Data ex_4_18_2; Input id x1 x2 time cens; Datalines; 1 12.3287 . 1.2156 1 2 10.9218 55.1459 7.9554 1 3 11.3121 57.0456 2.9250 1 4 . 58.5621 0.9621 1 5 . 52.3874 1.8050 1 6 . 50.4670 0.3094 1 7 12.2353 59.8188 2.1371 1 8 11.4210 58.1252 2.2197 1 9 11.5080 53.1021 6.9517 1 10 10.4844 57.5046 10.2008 1 11 . 54.9479 5.4848 1 12 11.8035 . 0.7029 1 13 . 56.9062 3.5465 1 14 11.2254 54.2180 3.2547 1 15 10.6788 59.8057 18.5000 0 16 12.6269 51.9732 0.1835 1 17 12.5793 51.1388 0.1338 1 18 11.2094 53.0963 2.1169 1 19 10.7267 . 18.1000 0 20 11.1453 56.0006 1.2938 1 21 10.7082 54.5896 7.5864 1 22 . . 0.7544 1 23 . 57.2073 4.2078 1 24 . 56.0106 0.5551 1 25 10.3376 . 2.0302 1 26 11.2606 52.8284 5.5608 1 27 12.9358 58.7939 1.6514 1 28 . 50.2757 0.7036 1 29 . . 17.1000 0 30 . 54.1234 4.3242 1 31 12.8282 53.9267 0.1889 1 32 . 54.2909 0.1652 1 33 11.0596 58.4193 2.9197 1 34 11.1375 . 0.0081 1 35 10.9177 . 3.0395 1 36 10.8543 . 16.4000 0 37 12.2401 55.6292 0.4115 1 38 11.1776 58.1161 14.3147 1 39 12.8818 50.6129 0.0999 1 40 10.3951 54.0858 0.2128 1 41 12.8831 56.1193 0.2371 1 42 11.7779 . 0.1301 1 43 12.3936 54.8616 0.0916 1 44 12.6147 . 2.7396 1 45 10.5717 52.4771 0.2278 1 46 10.6492 54.1929 9.3861 1 47 11.9100 51.1555 0.0598 1 48 10.2467 57.7899 15.1312 1 49 10.3359 55.9767 2.2812 1 50 10.9714 . 0.0133 1 51 11.4703 58.6877 2.3821 1 52 . 55.9946 8.4219 1 53 10.2551 55.4293 5.0773 1 54 10.0892 57.6119 1.8256 1 55 . 59.0977 1.6652 1 56 11.6243 52.7312 2.3398 1 57 10.7684 51.8873 8.7690 1 58 10.1591 50.2268 1.7924 1 59 10.0154 . 2.2419 1 60 12.1567 54.5669 6.9494 1 61 12.7212 . 1.9483 1 62 12.3659 56.3426 0.5164 1 63 11.3154 51.8964 1.0055 1 64 . 55.3102 0.3519 1 65 12.4114 58.4728 0.2433 1 66 11.2461 50.9602 0.1532 1 67 11.3202 58.1167 5.8493 1 68 12.0766 58.9753 1.6886 1 69 12.3359 . 3.7602 1 70 12.4174 52.9789 0.1029 1 71 . . 5.5158 1 72 11.0216 . 1.4579 1 73 12.1850 . 8.0417 1 74 . 53.0516 12.0522 1 75 12.4210 54.6284 1.3338 1 76 12.5339 51.4161 0.9457 1 77 . . 1.9716 1 78 10.6903 50.0855 0.5586 1 79 . 54.5308 0.0488 1 80 12.9477 54.1178 0.9272 1 81 10.5136 55.3421 6.9854 1 82 10.5545 59.2927 6.5906 1 83 11.7403 57.6955 0.5613 1 84 10.2464 . 11.6000 0 85 12.9755 . 0.1805 1 86 11.3005 . 0.7308 1 87 11.8805 56.8714 0.6732 1 88 . 51.0788 0.2373 1 89 11.8867 50.1781 0.0884 1 90 11.7378 56.4153 6.0328 1 91 10.3159 57.5350 10.9000 0 92 11.6738 . 1.8417 1 93 . 59.4420 10.7000 0 94 10.6474 59.4237 10.6000 0 95 10.9086 50.6709 0.8013 1 96 . 54.7673 10.4000 0 97 12.7734 . 0.0002 1 98 10.5732 56.3307 4.8323 1 99 10.5891 56.2606 7.8506 1 100 . 54.9770 6.2871 1 ; ************************************************** /*The following example code appears on page 161.*/ /*It produced output 5.2 and figure 5.1 */ ************************************************** /* Create artificial patient with time missing */ data add; c = 1; clark = 2 ; breslow = .4; run; /* Add artificial patient to data set */ data mel2; set melanoma add; proc lifereg data = mel2; model dfstime*dfscens(0) = clark breslow; output out = weibull cdf = cdf predicted = months quantiles = 0.02 to 0.98 by .02 control = c; data dfs; set weibull; dfs_est = (1 - _prob_)*100; proc print; var months dfs_est ; title 'Weibull Model with Clark = 2.0, Breslow = 0.4'; data; set dfs; if _n_ = 1 then do; dfs_est = 100 ; months = 0; output; end; output; title1 h=1.5 f=swissb 'Figure 5.1'; title2 h=1.5 f=swissb 'Weibull Model for Melanoma DFS'; title3 h=1.5 f=swissb 'Clark = 2.0, Breslow = 0.4'; axis1 width=2 minor=none label=(h=1.5 f=swissb a=90 j=center 'Pct DFS') value = (h = 1.2 f = swissb); axis2 width=2 label=(h=1.5 f=swissb 'Months') value=(h=1.2 f=swissb); symbol1 v=none l=1 i=spline w = 2 c = black; proc gplot; plot dfs_est*months / haxis = axis2 vaxis = axis1; run; ************************************************** /*The following example code appears on page 167.*/ /*It produced output 5.3 */ ************************************************** %paramest(t1=dfstime ,t2=dfscens /* Names of time variables. Defaults are t1 and t2. */ ,covs=clark breslow /* Names of the covariates */ ,method=2 /* Method of specifying time. Default is 2. */ ,hazard= theta[1]*theta[2]*theta[3]**x[1]*theta[4]**x[2]*t**(theta[2] - 1) /* Formula for the hazard function */ ,survival= exp(-theta[1]*theta[3]**x[1]*theta[4]**x[2]*t**theta[2]) /* Formula for the survival function */ ,init={1 1 1 1} /* Initial values for the parameters */ ,lower = {.0001 .0001 .0001 .0001} /* Lower bounds for parameters. Default is no lower bounds. */ ,upper = {10 10 10 10} /* Upper bounds for parameters. Default is no upper bounds. */ ) ************************************************** /*The following example code appears on page 168.*/ /*It produced output 5.4 */ ************************************************** data pred; do t = 10 to 500 by 10; clark = 2.0; breslow = 0.4; output; end; %paramest( dataset = melanoma ,t1 = dfstime , t2= dfscens /* Name of time variables. Defaults are is t1 and t2. */ ,covs = clark breslow /* Names of covariates */ ,hazard = theta[1]*theta[2]*theta[3]**x[1]*theta[4]**x[2]*t**(theta[2] - 1) /* Formula for the hazard function */ ,survival = exp(-theta[1]*theta[3]**x[1]*theta[4]**x[2]*t**theta[2]) /* Formula for the survival function */ ,init = {1 1 1 1} /* Initial values for the parameters */ ,lower = {.0001 .0001 .0001 .0001} /* Lower bounds for parameters. Default is no lower bounds. */ ,upper = {10 10 10 10} /* Upper bounds for parameters. Default is no upper bounds. */ ,pred = pred ); run; ************************************************** /*The following example code appears on page 170.*/ /*It produced output 5.5 */ ************************************************** data pred; do t = 0 to 12; output; end; title2 ?Exponential with Cure Model?; %paramest(dataset =leuk ,hazard = (1 - theta[1])*theta[2]*exp(-theta[2]*t)/ (theta[1] + (1 - theta[1])*exp(-theta[2]*t)) ,t1 = time ,t2 = d ,method = 2 ,survival = theta[1] + (1 - theta[1])*exp(-theta[2]*t) ,init={.6 .7} ,pred = pred ,lower = {.0001 .0001} ,upper = {.9999 .} ) %paramest( dataset =leuk ,hazard = theta[1]*exp(theta[2]*t) ,t1 = time ,t2 = d ,method = 2 ,survival=exp(-(theta[1]/theta[2])*(exp(theta[2]*t)-1)) ,init={.3 -.6} ,pred = pred ,lower = {.0001 .} ,upper = {. .} ) ************************************************** /*The following example code appears on page 173.*/ /*It produced output 5.6 */ ************************************************** data melanaoma; set melanoma; /* Define group = 1 if clark = 4, 2 otherwise */ group = 2 -(clark = 4); proc sort; by group; proc lifereg; model dfstime*dfscens(0) = ; by group; title 'Individual Groups'; run; proc lifereg; model dfstime*dfscens(0) =; title 'Combined Sample'; run; ******************************************************** /*The following example code appears on page 177 - 178.*/ /*It produced output 5.7 */ ******************************************************** %paramest(dataset=leuk ,hazard= exp(theta[3]+theta[4]*x[1])*(1-(1+exp(theta[1]+theta[2]*x[1]))** (-1))*exp(-exp(theta[3]+theta[4]*x[1])*t)/ ((1+exp(theta[1]+theta[2]*x[1]))**(-1) +(1-(1+exp(theta[1]+theta[2]*x[1]))**(-1))* exp(-exp(theta[3]+theta[4]*x[1])*t)) ,t1=years ,t2=cens ,covs=group ,method=2 ,survival= (1+exp(theta[1]+theta[2]*x[1]))**(-1) +(1-(1+exp(theta[1]+theta[2]*x[1]))**(-1))* exp(-exp(theta[3]+theta[4]*x[1])*t) ,init={0 0 .3 0} ) ******************************************************** /*The following example code appears on page 209. */ ******************************************************** data mydata; input patid @8 date_of_birth mmddyy8. @18 date_of_dx mmddyy8. cancertype $; /* Define age at diagnosis. */ age_at_dx = (date_of_dx - date_of_birth)/365; datalines; 21375 03/05/56 06/29/98 HD 36534 09/16/45 12/04/99 NHL 41224 11/17/62 . MEL ; ******************************************************** /*The following example code appears on page 212. */ /*It produced the output on page 213. */ ******************************************************** proc iml; /* Define the Gumbel pdf as the integrand */ start f(x); v = exp(x-exp(x)); return(v); finish; /* Define limits of integration, Call QUAD, and print results */ a = { .M 1 }; /* for -infinity to 1 */ call quad(z,"f",a); print z[format = 8.6]; run; ******************************************************** /*The following example code appears on page 214. */ /*It produced the output on page 215. */ ******************************************************** /* Invoke IML */ proc iml; /* establish constraints, lambda > 0 and gamma > 0 */ con = {.001 .001, . .}; /* convert data set to matrix xmatrix */ use times; read all var {time} into xmatrix; /* define loglikelihood function */ start loglik(theta) global (xmatrix); lambda = theta[1]; gamma = theta[2]; sum1 = 0; sum2 = 0; LL = 100*(log(lambda) + log(gamma)); do i = 1 to 100; x = xmatrix[i,1]; sum1 = sum1 + log(x); sum2 = sum2 + x**gamma; end; LL = LL + (gamma - 1)*sum1 - lambda*sum2; return(LL); finish loglik; /* options: first component specifies maximization second controls amount of printout */ optn = {1 2}; a = {0 0}; /* find initial feasible values, theta0 */ call nlpfea(theta0, a, con); /* invoke optimization program to find MLE, thetares */ call nlpnra(rc, thetares,"loglik",theta0 ,optn, con ,,,,,); /* find Hessian h */ call nlpfdd(f, g, h, "loglik", thetares); /* find covariance matrix of parameter estimates and print it */ cov = -inv(h); print cov; run; *********************************************************** /*The following example code appears on page 216 and 217. */ *********************************************************** %macro mle(dataset = _last_, n = , varname = , lower = , upper = , logpdf = ); proc iml; /* convert data set to matrix xmatrix */ use &dataset; read all var {&varname} into xmatrix; /* define loglikelihood function */ start loglik(theta) global (xmatrix); LL = 0; do i = 1 to &n; x = xmatrix[i,1]; LL = LL + &logpdf; end; return(LL); finish loglik; /* options: first component specifies maximization second argument controls amount of printing */ optn = {1 2}; con = &lower//&upper; k = ncol(con); a = j(1,k,0); /* find intitial feasible values, theta0 */ call nlpfea(theta0, a, con); /* invoke optimization program to find MLE, thetares */ call nlpnra(rc, thetares,"loglik",theta0 ,optn, con ,,,,,); /* find Hessian, h */ call nlpfdd(f, g, h, "loglik", thetares); /* find covariance matrix of parameter estimates and print it */ cov = -inv(h); print cov; run; %mend; %mle(dataset = times,varname = time, n = 100, lower = {.001 .001}, upper = { . .}, logpdf = log(theta[1]) + log(theta[2]) + (theta[2] - 1)*log(x) - theta[1]*x**theta[2]); ************************************************************************* ************************************************************************* MACRO CODE USED IN THIS BOOK The following macros are presented in this file: CHECKDIST KMTABLE KMPLAN KMPLOT LINRANK MANTBYAR PARAMEST PHPLOT PHPOW SURVPOW CONDPOW RAND_GEN RAND_PERM TEST PARAMEST ************************************************** /*The following macro appears on page 186. */ ************************************************** The CHECKDIST Macro %macro chekdist(data = _last_ , time = , cens = , censvals = 0 , model = , pi = ) ; %if "&model" = "exp" %then %do; proc lifetest data = &data noprint plots = (ls) graphics; time &time*&cens(&censvals); %end; %if "&model" = "weibull" %then %do; proc lifetest data = &data noprint plots = (lls) graphics; time &time*&cens(&censvals); %end; %if "&model" ne "weibull" and "&model" ne "exp" %then %do; proc lifetest data = &data noprint outs = out; time &time*&cens(&censvals); axis1 label = (a = 90); %end; %if "&model" = "lognorm" %then %do; data; set out; x = log(&time); y = -probit(survival); label x = 'log(t)'; label y = '-probit(survival)'; proc gplot; plot y*x /vaxis = axis1; %end; %if "&model" = "expcure" %then %do; data; set out; do pi = &pi ; y = log((survival - pi)/(1 - pi)); output; end; label y = 'log[(survival-pi)/(1-pi)]'; %end; %if "&model" = "gomp" %then %do; data; set out; do pi = π y=log(1-log(survival)/log(pi)); output; end; label y = 'log[1-log(survival)/log(pi)]'; %end; %if "&model" = "gomp" or "&model" = "expcure" %then %do; proc gplot; plot y*t = pi /vaxis = axis1; %end; %mend; ****************************************************** /*The following macro appears on page 48. */ ****************************************************** The KMTABLE Macro /* Note: Although the book says that you can use more than one by variable, that is not the case. Only one by variable can be specified. As a "workaround", you can create one by variable from several by contatenation. For example if you want tables by byvar1 and byvar2, define byvar = byvar1||byvar2. */ %macro kmtable(dataset=_last_ /* dataset used by macro */ ,pct=95 /* for conf. int. */ ,time= /* time variable */ ,cens= /* variable that indicates censored or complete time */ ,censval= 0 /* value(s) that indicate censored time */ ,method= /* 1 for conf. int. method used in Proc Lifetest, 2 for other*/ ,variance= /* G or g for Greenwoods formula, P or p for Petos */ ,byvar=none /* Optional by variable(s) */ ,print=yes /* no to suppress printing */ ); %global byvari perc ttime; %let byvari=&byvar; %let perc=&pct; %let ttime=&time; data &dataset; set &dataset; none=1; run; proc sort data=&dataset; by &byvar; run; /* Create dataset, x, with survival estimates */ proc lifetest noprint outs=x data=&dataset; by &byvar; time &time*&cens(&censval); run; data x; set x; retain temp; if survival ne . then temp = survival; if survival = . then survival = temp; proc sort out=y; by &byvar &time; run; /* Add number at risk, r, to dataset */ data y; set y; by &byvar; if first.&byvar then r=0; else r+1; keep r &byvar; run; /* Merge number at risk with survival estimates */ proc sort; by &byvar descending r; run; data table; merge x y; run; proc sort; by &byvar descending r; run; /* Create Life Table */ data table; set table; by &byvar; /* Allow for G or P, check for valid values, if mis-specified, set values and put warning in log. */ if _n_ = 1 then do; %if &variance = G %then %let variance=g; %if &variance = P %then %let variance=p; if "&variance" not in ('g', 'p') then do; put; put '*************************************'; put '*Note: Invalid value of variance used*'; put '*for choice of variance formula. g *'; put '*for Greenwood will be used. *'; put '*************************************'; put; end; if &method not in (1, 2) then do; put; put '*************************************'; put '*Note: Invalid value of method used*'; put '*for choice of CI. Method 1 (as in *'; put '*Proc Lifetest) will be used. *'; put '*************************************'; put; end; end; /* defaults for variance and conf int method */ %if &variance ne g and &variance ne p %then %let variance = g; %if &method ne 1 and &method ne 2 %then %let method = 1; /* normal critical value for conf. int. */ z=-probit((100-&pct)/200); d=1-_censor_; /* Peto s.e. */ sp=survival*sqrt((1-survival)/r); /* Greenwood s.e. */ if first.&byvar then do; sum=0; stderr=0; end; else do; *******************************************************************************************************; sum+d/(r*(r+1)); Change this to: sum+d/(r*(r-d)); ; *******************************************************************************************************; sg=survival*sqrt(sum); end; if "&variance"='g' then stderr=sg; if "&variance"='p' then stderr=sp; /* Confidence interval limits */ if &method=1 then do; lcl=survival-z*stderr; lcl=max(0,lcl); ucl=survival+z*stderr; ucl=min(1.00, ucl); end; if &method=2 then do; s=-stderr/log(survival)/survival; lcl=survival**(exp(z*s)); ucl=survival**(exp(-z*s)); end; if first.&byvar then do; stderr=0; lcl=1; ucl=1; end; /* Create column label for table */ %if &variance=g %then label stderr = 'Greenwood*stderr';; %if &variance = p %then label stderr = 'Peto*stderr';; %if &method = 1 %then %do; label lcl= "Method 1*&pct pct lcl";; label ucl="Method 1*&pct pct ucl";; %end; %if &method = 2 %then %do; label lcl = "Method 2*&pct pct lcl";; label ucl = "Method 2*&pct pct ucl";; %end; run; proc sort; by &byvar; run; /* Print life table */ %if &print = yes %then %do; proc print l split='*'; var &time d survival stderr lcl ucl; %if &byvar ne none %then by &byvar;; %end; run; %mend kmtable; ****************************************************** /*The following macro code appears on page 51. */ ****************************************************** The KMPLOT Macro %macro kmplot(mark=no /* yes to mark times on curve */ ,ci=no /* yes for conf. intervals */ ,ylabel= Pct Survival /* label for y axis */ ,xlabel=time /* label for x axis */ ,combine=no /* yes to put multiple plots on same graph */ ,cutoff=none /* clause to restrict curve(s), usually of form time &tau then varg = (surv(&t))**2*(z1/&n + &a/&n*z2); else varg = (surv(&t))**2*z1/&n; stderrg = sqrt(varg); print 'Greenwood Formula'; print "time = &t " varg[format=8.6] stderrg[format=8.6]; varp = surv(&t)*(1 - surv(&t))/&n; if &t > &tau then varp = varp*&a/(&a + &tau - &t); stderrp = sqrt(varp); print 'Peto Formula'; print "time = &t " varp[format=8.6] stderrp[format=8.6]; run; %mend; ****************************************************** /*The following macro code appears on page 95. */ ****************************************************** The LINRANK Macro %macro linrank(dataset=_last_, time=time, cens= , censval= ,groupvar= ,method=logrank,rho=1, stratvar= _none_, stratmis=no,trend=order ); /* Delete invalid observations and print list of observations deleted. */ data xx deleted; set &dataset; obsnumb = _n_; if &time <0 or &groupvar='' or &cens = . then delete=1; if "&stratvar" ne "_none_" and "&stratmis" = "no" and &stratvar = '' then delete =1; _none_ = 1; if delete=1 then output deleted; else output xx; proc print data=deleted; title 'Deleted Observations'; var obsnumb &time &cens &groupvar %if "&stratvar" ne "_none_" %then &stratvar;; /* Determine number of groups, their names, and the weights to use for trend test. */ proc sort data = xx; by &groupvar; data y; set xx; by &groupvar; if first.&groupvar then do; n+1; call symput('ngrps',left(n)); end; run; data grpnames; set y; by &groupvar; keep &groupvar n; if first.&groupvar; data groupwts; set grpnames; keep %if "&trend" = "order" %then n; %else &groupvar;; /* Find number of strata */ proc sort data=xx; by &stratvar; data xx; set xx; by &stratvar; retain stratn 0 ; if first.&stratvar then do; stratn+1; call symput('stratcnt', left(stratn)); end; run; /* Start loop on strata */ %do ii = 1 %to &stratcnt; /* Form stratum subset, find number of groups, number in each group, and group weights in stratum */ data x; set xx; if stratn = ⅈ call symput('stratval', &stratvar); run; proc freq; table &groupvar/ noprint out= counts; proc sort data=x; by &groupvar; data x; set x; by &groupvar; retain grpn 0 ; if first.&groupvar then do; grpn+1; call symput('grpcount', left(grpn)); call symput('grpname'||left(grpn), &groupvar); end; run; data grpnames; set x; by &groupvar; keep &groupvar grpn; if first.&groupvar; data grpwts; set grpnames; keep %if "&trend" = "order" %then grpn; %else &groupvar;; /* Create table */ proc sort data=x; by descending &time; data y; set x; keep r1-r&grpcount rtot; array r{*} r1-r&grpcount; retain r1-r&grpcount rtot 0; %let countsq = %eval(&grpcount*&grpcount); r{grpn}+1; rtot+1; data x; merge x y; proc sort; by &time; data x; set x; by &time; array d{*} d1-d&grpcount; retain d1-d&grpcount dtot; if first.&time then do i=1 to &grpcount; d{i}=0; dtot=0; end; if &cens not in (&censval) then do; d{grpn}+1; dtot+1; end; if last.&time then output; data x; set x; if dtot>0; retain km km_ 1; all=1; array e{*} e1-e&grpcount; array diff{*} diff1-diff&grpcount; array r{*} r1-r&grpcount; array d{*} d1-d&grpcount; array wdiff{*} wdiff1-wdiff&grpcount; array s{*} sum1-sum&grpcount; array cov{&grpcount, &grpcount} cov1-cov&countsq; array sumcov{&grpcount,&grpcount} sumcov1-sumcov&countsq; if _n_ = 1 then km_ = 1; else km_ = km; km=km*(rtot-dtot)/rtot; do j=1 to &grpcount; e{j} = dtot*r{j}/rtot; diff{j} = d{j} - e{j}; if "&method"="logrank" then w=1; if "&method"="gehan" then w=rtot; if "&method"="tarone" then w=sqrt(rtot); if "&method"="harrington" then w=km_**ρ wdiff{j} = w*diff{j}; s{j} + wdiff{j}; do l=1 to &grpcount; if dtot=1 then c=1; else c=(rtot-dtot)/(rtot-1); if j=l then cov{j,l}=w**2*(dtot*(rtot*r{j}-r{j}**2)*c)/rtot**2; else cov{j,l}=-w**2*(r{j}*r{l}*dtot*c)/rtot**2; sumcov{j,l}+cov{j,l}; end; end; /* Sum over times and reformat for printout */ proc means sum noprint; var d1-d&grpcount e1-e&grpcount diff1-diff&grpcount wdiff1-wdiff&grpcount; output out = out sum=; data out; set out; array e{*} e1-e&grpcount; array d{*} d1-d&grpcount; array difff{*} diff1-diff&grpcount; array wdif{*} wdiff1-wdiff&grpcount; do j = 1 to &grpcount; group = j; events = d{j}; expected = e{j}; diff = difff{j}; wdiff = wdif{j}; output; end; label wdiff = 'Weighted Diff'; label events = 'Events'; label expected = 'Expected'; label diff = 'Diff'; data xxx; merge out grpnames counts; proc print l noobs; var &groupvar count percent events expected diff wdiff; sum count events; title1 'Summary of Events vs Expected'; %if "&stratvar" ne "_none_" %then title2 "&stratvar = &stratval";; title3 "Method = &method"; run; /* Accumulate vectors and matrices for pooled stats */ %if "&ii" = "1" %then %do; data pooled; set xxx; %end; %else %do; data pooled; set pooled xxx; %end; data x; set x; proc sort; by all; data s (keep = sum1-sum&grpcount) cov (keep = col1-col&grpcount); set x; by all; if last.all; array s{*} sum1-sum&grpcount; array sumcov{&grpcount, &grpcount} sumcov1-sumcov&countsq; array col{*} col1-col&grpcount; output s; do j=1 to &grpcount; do l=1 to &grpcount; col{l}=sumcov{j,l}; end; output cov; end; data yy; merge grpnames cov; /* Give columns of covariance matrix group names */ %do j = 1 %to &grpcount; label col&j = "&&grpname&j"; %end; proc print l noobs; var &groupvar col1-col&grpcount; title1 'Covariance Matrix'; %if "&stratvar" ne "_none_" %then title2 "&stratvar= &stratval";; title3 "Method = &method"; %if "&ii" = "1" %then %do; data poolcov; set yy; %end ; %else %do; data poolcov; set poolcov yy; %end; /* Use proc iml to do matrix calculations for test statistic. */ proc iml; reset noprint; use s; read all into x; use cov; read all into v; use grpwts; read all var _all_ into grpwts; /* Omit first row and column */ xx=x[1:1,2:&grpcount]; vv=v[2:&grpcount,2:&grpcount]; stat= xx*inv(vv)*xx`; df = &grpcount - 1; p_val = 1-probchi(stat,df); results = stat||df||p_val; cols={ChiSquare df p_value}; title1 ' '; %if "&stratvar" ne "_none_" %then title1 "&stratvar= &stratval";; title2 "Method = &method"; print results[colname=cols]; /* Test for trend. */ if %eval(&grpcount) > 2 then do; wts=grpwts[2:&grpcount, 1:1]; xxx=xx*wts; vvv=wts`*vv*wts; stat = xxx*xxx/vvv; df=1; p_val= 1-probchi(stat,df); trend = stat||df||p_val; print trend[colname=cols]; end; quit; %end; /* end of loop on strata */ /* Pooled results if stratified analyis */ %if "&stratvar" ne "_none_" %then %do; proc freq data=xx; table &groupvar / noprint out=counts; proc sort data=pooled; by &groupvar; proc means noprint sum data=pooled; var count events expected diff wdiff; by group; output out=pooled1 sum=; data; merge pooled1 grpnames counts; proc print l noobs; var &groupvar count percent events expected diff wdiff; sum count events; title1 'Summary of Events vs Expected'; title2 "Pooled Over All Values of &stratvar"; proc sort data=poolcov; by &groupvar; proc means noprint sum data=poolcov; var col1-col&ngrps; by &groupvar; output out=pooled2 sum=; proc print l noobs; var &groupvar col1-col&ngrps; title1 'Covariance Matrix'; title2 "Pooled Over All Values of &stratvar"; data pooled2; set pooled2; keep col1-col&ngrps; run; proc iml; reset noprint; use pooled1; read all var {wdiff} into x; use pooled2; read all into v; xx=x[2:&ngrps,1:1]; vv=v[2:&ngrps,2:&ngrps]; stat = xx`*inv(vv)*xx; df = &ngrps - 1; p_val=1-probchi(stat,df); cols={ChiSquare df p_value}; title1 'Pooled Results'; title2 "Method = &method"; results = stat||df|| p_val; print results[colname=cols]; /* Test for trend. */ if %eval(&ngrps) > 2 then do; use groupwts; read all var _all_ into weights; wts = weights[2:&ngrps, 1:1]; xtrend = xx`*wts; vtrend = wts`*vv*wts; stattrnd = xtrend**2/vtrend; p_valtrd = 1-probchi(stattrnd,1); df=1; trend=stattrnd||df||p_valtrd; print trend[colname=cols]; run; end; %end; %mend; ****************************************************** /*The following macro code appears on page 105. */ ****************************************************** The MANTBYAR Macro %macro mantbyar(dataset= last_ , state= , time=time ,nstates=2 , format= default.,id=id); /* Create Format for States */ %let form=%str(proc format; value default 1='state1'); %do i=2 %to &nstates; %let form=&form &i= %str(%')STATE&i %str(%'); %end; &form; /* Create Table */ proc sort data= &dataset; by &id &time; data d; set &dataset end=last; by &id; retain id 0; if first.&id then id+1; if last then call symput('nobs',left(id)); proc sort; by &time descending &state; %let nstat2=%eval(&nstates*&nstates); %let nm1=%eval(&nstates-1); data dd; set d; by &time descending &state; retain s1-s&nobs r1-r&nstates o1-o&nstates; retain e1-e&nstates 0; retain v1-v&nstat2 0; retain d1-d&nstates 0; array s(*) s1-s&nobs; array r(*) r1-r&nstates; array e(*) e1-e&nstates; array o(*) o1-o&nstates; array ot(*) ot1-ot&nstates; array v(&nstates,&nstates) v1-v&nstat2; array d(*) d1-d&nstates; if &time=0 then do; s(id)=&state; r(&state)+1; nt+1; end; if &time>0 and &state<&nstates+1 then do; prior=s(id); r(prior)+(-1); if &state>0 then r(&state)+1; s(id)=&state; if &state=0 then nt+(-1); end; if &state=&nstates+1 then do; if first.&state then do i=1 to &nstates; ott=0; ot(i)=0; end; prior=s(id); ot(prior)+1; ott+1; /* Calculate covariance matrix */ if last.&state then do; do i=1 to &nstates; do j= 1 to &nstates; if i=j then v(i,i)+r(i)*(nt-r(i))*ott*(nt-ott)/(nt**2*(nt-1)); else v(i,j)+(-r(i)*r(j)*ott*(nt-ott)/(nt**2*(nt-1))); end; end; do i=1 to &nstates; e(i)+r(i)/nt*ott; r(i)+(-ot(i)); o(i) + ot(i); d(i)+ (o(i)-e(i)); end; nt+(-ott); output; end; end; data ddd; set dd end=last; array e(*) e1-e&nstates; array o(*) o1-o&nstates; if last; df=&nstates-1; do &state=1 to &nstates; expected=e(&state); observed=o(&state); ratio=observed/expected; diff=observed-expected; output; end; data cov; set ddd; keep v1-v&nstat2; %let slist=; %do i=1 %to &nstates; data _null_ ; call symput('s', put(&i,&format)); run; %let slist= &slist &s ; %end; data exp ; set ddd; keep e1-e&nstates; data obs; set ddd; keep o1-o&nstates; /* Use IML to calculate test statistic and print results */ proc iml; use cov; read into covmat; cov=shape(covmat,&nstates); statlist={&slist}; tranlist=statlist'; use exp; read into expmat; print 'Covariance Matrix'; print cov[r=tranlist c=statlist format=10.4]; v=diag(cov); use obs; read into obsmat; rmat=obsmat#expmat##(-1); r=rmat'; difmat=obsmat-expmat; d=difmat'; obs=obsmat'; exp=expmat'; z=difmat*inv(v##(.5)); p=(1-probnorm(abs(z)))'; p=2*p; state =obs||exp||r||d||p; top={'Observed' 'Expected' 'O/E' 'O-E' 'P-Value'}; print 'Summary of Results for Each State'; print state[r=tranlist c=top format=12.4]; cov=cov[1:&nm1,1:&nm1]; expmat =expmat[1,1:&nm1]; obsmat=obsmat[1,1:&nm1]; chisq=(expmat-obsmat)*inv(cov)*(expmat-obsmat)'; print 'Test of Homogeneity of States'; p=1-probchi(chisq,&nm1); df=&nm1; print chisq [format=11.4] df [format=5.0] p [format=8.4] ; %mend mantbyar; ************************************************** /*The following macro code appears on page 183. */ ************************************************** The PARAMEST Macro %macro paramest(dataset = _last_, t1=t1, t2=t2, covs=none, method = 2, hazard = , survival = ,init= , alpha = 0.05, lower = {. . . . . . . . . .}, upper = {. . . . . . . . . .}, pred = x, censval = {0}); /*Remove Observations with Time Values That Are Not Permissable*/ data checked; set &dataset; if &method=1 and ((&t1 = . and &t2 = .) or &t1 > &t2 or . < &t1 < 0 or . < &t2 < 0) then delete; if &method=2 and (&t1 < 0 or &t2 = . ) then delete; /* to get rid of previous datasets */ data survival; set _null_; data x; set _null_; proc iml; b = &upper; a = &lower; con = a//b; use checked; %if &covs = none %then %str(read all var {&t1 &t2} into vars;); %else %str(read all var {&t1 &t2 &covs} into vars;); nvars = ncol(vars); ncovs = nvars - 2; nobs = nrow(vars); times = vars[, 1:2]; /* convert to method 1 for time and censoring data */ if &method = 2 then do i=1 to nobs; if sum((times[i,2] = &censval)) >0 then times[i,2] = .; else times[i,2] = times[i,1]; end; /* module to calculate log likelihood */ start loglik(theta) global (times,vars,nobs,nvars); LL=0; do i=1 to nobs; /* get covariates */ if nvars > 2 then x = vars[i,3:nvars]; /* right censored time */ if (times[i,1] ^= .) & (times[i,2] = . ) then do; t = times[i,1]; LL = LL + log(&survival); end; /* left censored time */ if (times[i,1] = .) & (times[i,2] ^= .) then do; t = times[i,2]; LL = LL + log(1 - (&survival)); end; /* interval censored time */ if (times[i,1] ^= .) & (times[i,2] > times[i,1]) then do; t = times[i,1]; temp = &survival ; t = times[i,2]; y = temp - (&survival) ; LL = LL + log(y); end; /* uncensored time */ if times[i,1] = times[i,2] then do; t=times[i,1]; LL = LL + log(&hazard) + log(&survival); end; end; return(LL); finish loglik; theta0= &init`; nparams = ncol(&init); con = con[, 1:nparams]; optn = {1 0}; /* call optimization function */ call nlptr(rc,thetares,"loglik",theta0,optn,con,,,,,); thetaopt=thetares`; maxll=loglik(thetaopt); /* rc is return code - negative means failed to converge */ if rc < 0 then print "Iteration failed to converge. Estimates are unreliable."; if rc > 0 then do; print "Successfull Convergence"; print maxll " Is Maximum Loglikelihood."; end; /* module to calculate first derivs (deriv) and second derivs (h)*/ call nlpfdd(LL,deriv,h,"loglik", thetaopt); /* get covariance matrix and standard errors of estimates */ cov = -inv(h); setheta=sqrt(vecdiag(cov)); theta=thetaopt; use &pred; read all var _all_ into values; n = nrow(values); survival = j(1,n,0); sesurv = j(1,n,0); covplus1 = ncovs + 1; /* calculate survival function for t and covariates in pred dataset, as well as statndard error using delta method */ do i = 1 to n; if ncovs > 0 then x = values[i,2:covplus1]; t = values[i,1]; start surv(theta) global (i, t, x); surv = &survival; return(surv); finish surv; call nlpfdd(s,deriv,hess,"surv", theta); survival[i] = s; sesurv[i] = sqrt(deriv*cov*deriv`); end; survival = survival`; sesurv = sesurv`; create thetas from theta[colname = 'theta']; append from theta; create setheta from setheta[colname = 'stderr']; append from setheta; create cov from cov; append from cov; create survival from survival[colname = 'survival']; append from survival; create sesurv from sesurv[colname = 'stderr']; append from sesurv; data thetas; merge thetas setheta; c = probit(1 - &alpha/2); /* (1 - alpha)100% CI */ lower = theta - stderr*c; upper = theta + stderr*c; proc print data = thetas; var theta stderr lower upper; title 'Parameter Estimates'; run; proc means noprint n data = cov; var col1; output out = out n = nparams; run; data; set out; call symput('n', nparams); run; data names; %do i = 1 %to &n; name = "theta&i"; output; %end; run; data cov; merge names cov; proc print data= cov noobs label ; %do i = 1 %to &n; label col&i = "theta&i"; %end; label name = 'Cov'; title 'Estimated Covariance Matrix'; run; /* merge survival estimates and stderrors with pred dataset */ data table; merge &pred survival sesurv ; proc print data = table noobs; title 'Estimated Survival Probabilities'; run; %mend; ************************************************** /*The following macro code appears on page 150. */ ************************************************** The PHPLOT Macro %macro phplot(data = , ci=no, yvar=survival, ylabel=Pct Survival, byvar= none, xvar=time,xlabel=Time,combine=no, title='Proportional Hazards Survival Curve', lcl=lcl, ucl = ucl ); /* Symbol statements for up to 4 curves on one graph */ %if &combine=yes %then %do; %let ci=no; symbol1 l=1 v=none i=stepjl w=5; symbol2 l=3 v=none i=stepjl w=5; symbol3 l=5 v=none i=stepjl w=5; symbol4 l=33 v=none i=stepjl w=5; %end; /* Symbol Statements for separate graphs */ %if &combine=no %then %do; symbol1 l=1 v= none i=stepjl w=5; symbol2 l=3 v=none i=stepjl w=5; symbol3 l=3 v=none i=stepjl w=5; %end; %if &byvar=none %then goptions cby=white;; data; set &data; survival=&yvar*100; lcl=100*&lcl; ucl=100*&ucl; y=survival; curve=1; output; y=ucl; curve=2; %if &ci=yes %then output;; y=lcl; curve=3; %if &ci=yes %then output;; proc sort; by &byvar curve &xvar; run; proc format; value curve 1='PH curve' 2='UCL' 3='LCL'; axis1 width=5 minor=none label=(h=2 f=swiss a=90 j=center "&ylabel")value=(h=1.5 f=swiss) order=(0 to 100 by 10); axis2 width=5 label=(h=2 f=swiss "&xlabel") value=(h=1.5 f=swiss); %if &combine=no %then legend1 label=(f=swiss h=1.5 'Curve') value=(f=swiss h=1.5 j=l 'PH Curve' "UCL" "LCL");; legend2 label=(f=swiss h=1.5) value=(f=swiss h=1.5 j=l); /* PROC GPLOT for separate graphs */ %if &combine=no %then %do; proc gplot; plot y*&xvar= curve / legend=legend1 vaxis=axis1 haxis=axis2 %if &ci=no %then nolegend;; ; by &byvar; format curve curve.; %end; /* PROC GPLOT for combined graphs */ %if &combine=yes %then %do; proc gplot; plot y*&xvar=&byvar/ legend=legend2 vaxis=axis1 haxis=axis2; %end; title &title; run; %mend phplot; ************************************************** /*The following macro code appears on page 151. */ /*It is an improvement of a macro of the same */ /*that appears in the first edition of this book.*/ ************************************************** The PHPOW Macro %macro phpow(t = , tau = , alpha = .05, n = ., power = ., var = , delta = , s0 = ); /*This is a SAS macro for calculating power and sample size. It is based on Hsieh and Lavori (2000), Controlled Clinical Trials, 21: 552-560*/ proc iml; file print; title 'Power and Sample Size Results'; /*The inner integrand is calculated*/ start integrand(time) global(yv,delta,var); pi = 4*atan(1); part1 = 1/sqrt(2*pi*var)*exp(-yv**2/(2*var)); part2 = 1 - (&s0)**(delta**yv); p = part1*part2; return(p); finish; /*The inner integral is calculated*/ start marginal(v) global(yv,t, tau ); tt = t; ttau = tau; upper = tt + ttau; interval = ttau||upper; yv = v; call quad(pm,"integrand",interval); return(pm); finish; /*The outer integral and probability of death are calculated*/ start outer(tt,ttau, svar, ddelta) global( t, tau, var, delta ); t = tt; tau = ttau; var = svar; delta = ddelta; interval= .M ||.P; call quad(per,"MARGINAL",interval); prob_d = per/t; return(prob_d); finish; prob_d = outer(&t, &tau, &var, &delta); /* Power is calculated if it is missing */ %if &power = . %then %do; deaths = prob_d*&n; zpower = sqrt(&var)*abs(log(&delta))*sqrt(deaths)- probit(1 - &alpha/2); power_calculated = round(probnorm(zpower), .1); sample_size = &n; %end; /* Sample size is calculated if it is missing */ %else %do; zpower = probit(&power); deaths = (zpower+probit(1-&alpha/2))**2/(abs(log(&delta)))**2/&var; sample_size_calculated = round(deaths/prob_d, 1); power = &power; %end; /*Output results*/ Alpha = α hazard_ratio = δ accrual_time = &t; followup_time = τ covariate_variance = &var; baseline_survival = ?&s0?; put 'Alpha ='alpha; put; put 'Hazard Ratio ='hazard_ratio; put; put 'Accrual Time ='accrual_time; put; put 'Followup Time ='followup_time; put; put 'Covariate Variance ='covariate_variance; put; put Baseline Survival = baseline_survival; put; %if &power=. %then %do; put 'Sample Size =' sample_size; put; put 'Power(Calculated) ='power_calculated; put; %end; %else %do; put 'Power ='power; put; put 'Sample Size (Calculated) =' sample_size_calculated; %end; run; %mend; ****************************************************** /*The following macro code appears on page 103. */ ****************************************************** The SURVPOW Macro %macro survpow(s1= , s2= , nsub=365, actime= ,futime= , rate= ,p=.5, loss1=0, loss2=0, w=1, siglevel=.05) ; /* Find number of points in data set for group 1 and convert to vectors */ data _null_; set &s1; i=_n_; call symput('counta',left(i)); run; data y; set &s1; retain sa1-sa&counta ta1-ta&counta ; array surv{*} sa1-sa&counta; array ttime{*} ta1-ta&counta; t=t*⊄ all=1; i=_n_; surv{i}=s; ttime{i}=t; output; proc sort; by all; data y; set y; by all; if last.all; keep all ta1-ta&counta sa1-sa&counta; /* Find number of points in data set for group 2 and convert to vector */ data _null_; set &s2; i=_n_; call symput('countb', left(i)); run; data yy; set &s2; retain sb1-sb&countb tb1-tb&countb; array surv{*} sb1-sb&countb; array ttime{*} tb1-tb&countb; t=t*⊄ all=1; i=_n_; surv{i}=s; ttime{i}=t; output; proc sort; by all; data yy; set yy; by all; if last.all; keep all tb1-tb&countb sb1-sb&countb; /* Find hazards at each partition point */ data z; all=1; do t=0 to (&actime+&futime)*&nsub - 1 ; output; end; proc sort; by all; data merged; merge z y yy; by all; if trim("&counta") = "1" then lam1=-log(sa1)/ta1; %do i=1 %to &counta -1 ; %let j = %eval(&i+1); if ta&i le t lt ta&j then lam1 = (sa&i-sa&j)/((sa&j-sa&i)*(t-ta&i)+sa&i*(ta&j-ta&i)); %end; if trim("&counta") = "2" and ta2 = . then do; lambda = -log((sa1-sa2)/(1-sa2))/ta1; lam1 = lambda*(1-sa2)*exp(-lambda*t)/(sa2+(1-sa2)*exp(-lambda*t)); end; if trim("&countb") = "1" then lam2=-log(sb1)/tb1; %do i=1 %to &countb -1 ; %let j = %eval(&i+1); if tb&i le t lt tb&j then lam2 = (sb&i-sb&j)/((sb&j-sb&i)*(t-tb&i)+sb&i*(tb&j-tb&i)); %end; if trim("&countb") = "2" and tb2 = . then do; lambda = -log((sb1-sb2)/(1-sb2))/tb1; lam2 = lambda*(1-sb2)*exp(-lambda*t)/(sb2+(1-sb2)*exp(-lambda*t)); end; /* Calculate ratio of hazards and number at risk at each partition point and accumulate needed sums */ data next; set merged; by all; retain n1 n2 n; if _n_ = 1 then do; n1=&rate*&p*&actime; n2=&rate*(1-&p)*&actime; n=n1+n2; end; tau=&futime*⊄ psi1=&loss1/⊄ psi2=&loss2/⊄ phi=n1/n2; theta=lam1/lam2; d1=lam1*n1; d2=lam2*n2; d=d1+d2; c1=psi1*n1; c2=psi2*n2; if t > tau then do; c1=c1+n1/(&actime*&nsub+tau-_n_+1); c2=c2+n2/(&actime*&nsub+tau-_n_+1); end; n1=n1-d1-c1; n2=n2-d2-c2; sum1+(d*&w*(phi*theta/(1+phi*theta) - phi/(1+phi))); sum2+d*&w**2*phi/(1+phi)**2; n=n1+n2; /* Calculate e and power */ if last.all then do; e=sum1/sqrt(sum2); z=-probit(&siglevel/2); power = 1 - probnorm(z-e) + probnorm(-z-e); ac_time=symget('actime'); fu_time=symget('futime'); ac_rate=symget('rate'); n=ac_rate*ac_time; alpha = symget('siglevel'); prop=symget('p'); los_rat1=symget('loss1'); los_rat2=symget('loss2'); weights = symget('w'); output; end; label ac_time='Accrual Time'; label power='Power'; label fu_time = 'Followup Time'; label ac_rate = 'Accrual Rate'; label n = 'N'; label prop = 'Prop in Grp 1'; label los_rat1 = 'Loss Rate 1'; label los_rat2 = 'Loss Rate 2'; label weights = 'Weights'; /* Print results */ proc print l noobs; var ac_time fu_time ac_rate n alpha prop los_rat1 los_rat2 weights power; /* data results; set results next; */ run; %mend; ****************************************************** /*The following macro code appears on page 107. */ ****************************************************** %macro condpow(dataset=_last_,lam1=,lam2=, atime=,arate=,follow=, grp1=.5, pi1=, pi2=,stat=Log-Rank,alpha=.05, n=); options printmsglist=0 source=0 notes=0; **to avoid excessive log; ods listing close; **to avoid excessive output; **creating 'all' dataset for record the output of lifetest; data all; probchisq= .; run; **begin the loop for n replications of lifetest; %do i=1 %to &n; **bring the existing dataset; %if "&dataset" ne "none" %then %do; data b; set &dataset; cure1=(&pi1/(&pi1+(1-&pi1)*exp(-&lam1*time))); cure2=(&pi2/(&pi2+(1-&pi2)*exp(-&lam2*time))); rand1=ranuni(0); rand2=ranuni(0); if cens=0 and group=1 and rand1 < cure1 then addtime=1000000; else if cens=0 and group=1 and rand1 >=cure1 then addtime= ranexp(0)/&lam1; else if cens=0 and group=2 and rand2 < cure2 then addtime= 1000000; else if cens=0 and group=2 and rand2 >=cure2 then addtime= ranexp(0)/&lam2; else addtime=0; if cens=0 and addtime >= (&atime+&follow) then do; survtime= time + (&atime + &follow); endcens=0; end; else if cens=0 and addtime < (&atime +&follow) then do; survtime =time + addtime ; endcens=1; end; else if cens=1 then do; survtime = time; endcens= 1; end; else if cens=-1 then do; survtime = time; endcens= 0; end; drop cure1 cure2 rand1 rand2; %end; **allow the power estimate without existing data set; data c; do j=1 to &arate*&atime; accurday=ranuni(0)*&atime; group=(ranuni(0)>&grp1) + 1; piran1=ranuni(0); piran2=ranuni(0); if group=1 and piran1 <&pi1 then addtime=1000000; else if group=1 and piran1 >=&pi1 then addtime=ranexp(0)/&lam1; else if group=2 and piran2 <&pi2 then addtime=1000000; else if group=2 and piran2 >=&pi2 then addtime=ranexp(0)/&lam2; if addtime >=(accurday + &follow) then do; survtime = accurday + &follow; endcens=0; end; else if addtime <(accurday +&follow) then do; survtime = addtime; endcens=1; end; output; end; run; **combine the new dataset b and c together for lifetest; %if "&dataset" ne "none" %then %do; data d (keep=survtime endcens group); set b c; run; %end; %else %if "&dataset" = "none" %then %do; data d (keep=survtime endcens group); set c; %end; run; **Do lifetest 1 to i times and write the output to the out&i files; ods output homtests=out&i; proc lifetest data=d notable; time survtime*endcens(0); strata group; run; data out&i; set out&i; if test = "&stat"; run; data all; set all out&i; run; %end; **calculating the power as the # of reject Ho vs. the # of total lifetests; data all; set all; if probchisq ne .; if probchisq < &alpha then reject+1; power=reject/(_N_ - 1); n=_N_ -1; if n =&n then do; ***put results into log; put; put; put "Results for Conditional Power Simulation for &stat test" ; put; put "Existing Dataset = &dataset Significance Level = &alpha"; put 'After ' n 'Replications, Estimated Power = ' Power; put "Group1 percentage = &grp1 Group1 lamda = &lam1 Group1 cure rate = &pi1"; put "Group2 lamda = &lam2 Group2 cure rate = &pi2"; put "accrual time = &atime accrual rate = &arate follow up time = &follow" ; end; run; %mend condpow; ****************************************************** /*The following macro code appears on page 101. */ ****************************************************** %macro perm_gen(indata = &syslast, time = , cens = , n1 = , n2 = , group =); %let n = %eval(&n1 + &n2); %let ncomb = %sysfunc(comb(&n, &n1)); ods output Plan=Combinations; proc plan ; factors replicate= &ncomb ordered r= &n1 of &n comb; run; data reps; set combinations; keep replicate i &group ; array r{*} r1 - r&n1; array grp{*} group1 - group&n; do i = 1 to &n; grp{i} = 2; do j = 1 to &n1; if r{j} = i then grp{i} = 1; end; &group = grp{i}; output; end; run; data temp; set _null_; %do i = 1 %to &ncomb; data temp; set temp &indata; keep &time &cens; %end; data reps; merge reps temp; data temp2; set &indata; replicate = 0; data reps; set temp2 reps; run; %mend; ****************************************************** /*The following macro code appears on page 100. */ ****************************************************** %macro rand_gen( indata= , time = , cens = , group = , numreps=1000, seed=0); %let indata=&indata; /* forces evaluation of &INDATA at the right time */ /* Get size of input dataset into macro variable &NUMRECS */ proc sql noprint; select count(*) into :numrecs from &INDATA; quit; /* Generate &NUMREPS random numbers for each record, so records can be randomly sorted within each replicate */ data __temp_1; retain seed &SEED ; drop seed; set &INDATA; do replicate = 1 to &NUMREPS; call ranuni(seed,rand_dep); output; end; run; proc sort data=__temp_1; by replicate rand_dep; run; /* Now append the new re-orderings to the original dataset. Label the original as Replicate=0, so the %TEST macro will be able to pick out the correct p-value. Then use the ordering of __counter within each replicate to write the original values of &time and &cens, thus creating a randomization of these variables in every replicate. */ data reps ; array timelist{ &NUMRECS } _temporary_ ; array censlist{ &NUMRECS } _temporary_; set &INDATA(in=in_orig) __temp_1(drop=rand_dep); if in_orig then do; replicate=0; timelist{_n_} = &time ; censlist{_n_} = &cens ; end; else do ; &time = timelist{ 1+ mod(_n_,&NUMRECS) }; &cens = censlist{ 1+mod(_n_, &NUMRECS) }; end; run; %mend rand_gen; ****************************************************** /*The following macro code appears on page 102. */ ****************************************************** %macro test(time = , cens = , censval = , test = , group = , type = ); proc lifetest data = reps outtest = out noprint; time &time*&cens(&censval); test &group; by replicate; run; data out2 ; set out; if "&test" = 'logrank' then type = 'LOG RANK'; if "&test" = 'gehan' then type = 'WILCOXON'; if _TYPE_ = type and _NAME_ = "&time" then output; data out3; set out2 end = last; retain chisq; if replicate = 0 then chisq = &time; else do; if &time + .00000001 ge chisq then num+1; end; if last then do; pvalue = num/(_n_ - 1); stderr = sqrt((pvalue*(1-pvalue))/(_n_ - 1)); lowbound = max(pvalue - 1.96*stderr, 0); upperbound = min(pvalue + 1.96*stderr, 1); n = _n_ - 1; output; end; %if &type = rand %then %do; label n = 'Number of Replicates'; label pvalue = "Randomization &test Test Estimated P-Value (2-sided)"; label lowbound = 'Lower 95 Pct Bound'; label upperbound = 'Upper 95 Pct Bound'; %end; %else %do; label pvalue = "Permutation &test Test P-Value (2-sided)"; %end; %if &type = rand %then %do; proc print noobs l; var pvalue stderr lowbound upperbound n; %end; %else %do; proc print noobs l ; var pvalue; %end; run; data; set out2; if replicate = 0; p = 1 - probchi(&time, 1); label p = 'Asymptotic P-Value'; proc print noobs l ; var p; run; %mend; ****************************************************** /*The following macro code appears on page 183. */ ****************************************************** %macro paramest(dataset = _last_, vars = t1 t2, method = 2, hazard = , survival = ,init= , alpha = 0.05, lower = {. . . . . . . . . .}, upper = {. . . . . . . . . .}, pred = x, censval = {0}); /*Remove Observations with Time Values That Are Not Permissable*/ data checked; set &dataset; if &method=1 and ((&t1 = . and &t2 = .) or &t1 > &t2 or . < &t1 < 0 or . < &t2 < 0) then delete; if &method=2 and (&t1 < 0 or &t2 = . ) then delete; /* to get rid of previous datasets */ data survival; set _null_; data x; set _null_; proc iml; b = &upper; a = &lower; con = a//b; use checked; read all var {&vars} into vars; nvars = ncol(vars); ncovs = nvars - 2; nobs = nrow(vars); times = vars[, 1:2]; /* convert to method 1 for time and censoring data */ if &method = 1 then do i=1 to nobs; if sum((times[i,2] = &censval)) >0 then times[i,2] = .; else times[i,2] = times[i,1]; end; /* module to calculate log likelihood */ start loglik(theta) global (times,vars,nobs,nvars); LL=0; do i=1 to nobs; /* get covariates */ if nvars > 2 then x = vars[i,3:nvars]; /* right censored time */ if (times[i,1] ^= .) & (times[i,2] = . ) then do; t = times[i,1]; LL = LL + log(&survival); end; /* left censored time */ if (times[i,1] = .) & (times[i,2] ^= .) then do; t = times[i,2]; LL = LL + log(1 - &survival); end; /* interval censored time */ if (times[i,1] ^= .) & (times[i,2] > times[i,1]) then do; t = times[i,1]; temp = &survival ; t = times[i,2]; y = temp - &survival ; LL = LL + log(y); end; /* uncensored time */ if times[i,1] = times[i,2] then do; t=times[i,1]; LL = LL + log(&hazard) + log(&survival); end; end; return(LL); finish loglik; theta0= &init`; nparams = ncol(&init); con = con[, 1:nparams]; optn = {1 0}; /* call optimization function */ call nlptr(rc,thetares,"loglik",theta0,optn,con,,,,,); thetaopt=thetares`; maxll=loglik(thetaopt); /* rc is return code - negative means failed to converge */ if rc < 0 then print "Iteration failed to converge. Estimates are unreliable."; if rc > 0 then do; print "Successfull Convergence"; print maxll " Is Maximum Loglikelihood."; end; /* module to calculate first derivs (deriv) and second derivs (h) */ call nlpfdd(LL,deriv,h,"loglik", thetaopt); /* get covariance matrix and standard errors of estimates */ cov = -inv(h); setheta=sqrt(vecdiag(cov)); theta=thetaopt; use &pred; read all var _all_ into values; n = nrow(values); survival = j(1,n,0); sesurv = j(1,n,0); covplus1 = ncovs + 1; /* calculate survival function for t and covariates in pred dataset, as well as statndard error using delta method */ do i = 1 to n; if ncovs > 0 then x = values[i,2:covplus1]; t = values[i,1]; start surv(theta) global (i, t, x); surv = &survival; return(surv); finish surv; call nlpfdd(s,deriv,hess,"surv", theta); survival[i] = s; sesurv[i] = sqrt(deriv*cov*deriv`); end; survival = survival`; sesurv = sesurv`; create thetas from theta[colname = 'theta']; append from theta; create setheta from setheta[colname = 'stderr']; append from setheta; create cov from cov; append from cov; create survival from survival[colname = 'survival']; append from survival; create sesurv from sesurv[colname = 'stderr']; append from sesurv; data thetas; merge thetas setheta; c = probit(1 - &alpha/2); /* (1 - alpha)100% CI */ lower = theta - stderr*c; upper = theta + stderr*c; proc print data = thetas; var theta stderr lower upper; title 'Parameter Estimates'; run; proc means noprint n data = cov; var col1; output out = out n = nparams; run; data; set out; call symput('n', nparams); run; data names; %do i = 1 %to &n; name = "theta&i"; output; %end; run; data cov; merge names cov; proc print data= cov noobs label ; %do i = 1 %to &n; label col&i = "theta&i"; %end; label name = 'Cov'; title 'Estimated Covariance Matrix'; run; /* merge survival estimates and stderrors with pred dataset */ data table; merge &pred survival sesurv ; proc print data = table noobs; title 'Estimated Survival Probabilities'; run; %mend; The Macro Chekdist %macro chekdist(data = _last_ , time = , cens = , censvals = 0 , model = , pi = ) ; symbol1 v = plus c = black; symbol2 v = dot c = black; symbol3 v = circle c = black; symbol4 v = diamond c = black; symbol5 v = star c = black; %if "&model" = "exp" %then %do; proc lifetest data = &data noprint plots = (ls) ; time &time*&cens(&censvals); %end; %if "&model" = "weibull" %then %do; proc lifetest data = &data noprint plots = (lls) ; time &time*&cens(&censvals); %end; %if "&model" ne "weibull" and "&model" ne "exp" %then %do; proc lifetest data = &data noprint outs = out; time &time*&cens(&censvals); axis1 label = (a = 90); %end; %if "&model" = "lognorm" %then %do; data; set out; x = log(&time); y = -probit(survival); label x = 'log(t)'; label y = '-probit(survival)'; proc gplot; plot y*x /vaxis = axis1; %end; %if "&model" = "expcure" %then %do; data; set out; do pi = &pi ; y = log(survival - pi); output; end; label y = 'log[(survival-pi)]'; %end; %if "&model" = "gomp" %then %do; data; set out; do pi = π y=log(1-log(survival)/log(pi)); output; end; label y = 'log[1-log(survival)/log(pi)]'; %end; %if "&model" = "gomp" or "&model" = "expcure" %then %do; proc gplot; plot y*&time = pi /vaxis = axis1; %end; run; %mend;