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/****************************************************************/
/* S A S S A M P L E L I B R A R Y */
/* */
/* NAME: PHRSURV */
/* TITLE: Covariate-Adjusted Survivor Function */
/* PRODUCT: STAT */
/* SYSTEM: ALL */
/* KEYS: survival analysis, adjusted survivor function */
/* PROCS: PHREG, SGPLOT */
/* DATA: */
/* */
/* SUPPORT: sasycs UPDATE: */
/* MISC: */
/* */
/****************************************************************/
/*
How should one report the survival curves after fitting a
Cox model? Of course one can get specific curves corresponding
to covariate profiles from the BASELINE statement. But often
in an applied setting, one would like to estimate the survival
curves for various levels of TREATMENT with all other
covariates treated as fixed. Hosmer-Lemeshow (1999, p. 145)
suggest an estimator based on the median of the risk scores on
the remaining covariates. Marubini et al (1995) advocate the
direct-adjusted estimator of Makuch (1982).
Hosmer, D.W. and Lemeshow, S. (1999) Applied Survival Analysis:
Regression Modelling of Time to Event Data. Wiley.
Makuch, R.W. (1992) "Adjusted survival curve estimation using
covariates," Journal of Chronic Disease, 35, pp437-443.
Marubini E. and Valsecchi, M.G. (1995) Analysis Survival Data
from Clinical Trials and Observational Studies. Wiley
*/
/*
The data set in section 1.3 of Hosmer and Lemeshow (1999) is
used to illustrate how to compute these two covariate-adjusted
estimates of the survivor function for each of the long and
short treatment program.
*/
Data UIS;
input Id Time Censor Treat Age_C Drug NDrgTx_C;
label Time='Time from Admission to Relapse (Days)'
Censor='1=Return to Drug 0=Otherwise'
Treat='Treatment Program: 0=Short 1=Long'
Age_C='Age - 35'
Drug='Drug Use: 0=No 1=Yes'
NDrgTx_C='No of Prior Drug Treatments - 3';
datalines;
1 188 1 1 9 1 -2
2 26 1 1 3 1 5
3 207 1 1 3 1 0
4 144 1 0 2 1 -2
5 551 0 1 -6 0 2
6 32 1 1 0 1 -2
7 459 1 1 9 1 31
8 22 1 1 -3 1 -1
9 210 1 1 10 1 0
10 184 1 1 6 1 4
12 212 1 1 8 1 5
13 87 1 1 -1 0 -2
14 598 0 1 2 1 -1
15 260 1 1 11 1 5
16 210 1 1 1 1 -2
17 84 1 1 -3 1 0
18 196 1 1 -2 1 3
19 19 1 1 -2 1 -2
20 441 1 1 6 0 12
21 449 1 1 2 1 2
22 659 0 0 3 0 -2
23 21 1 0 -2 1 5
24 53 1 0 -1 1 -3
25 225 1 0 5 1 6
26 161 1 0 15 1 3
27 87 1 0 5 1 2
28 89 1 0 -6 0 0
29 44 1 0 6 1 4
30 37 1 0 -3 1 27
31 523 0 0 9 1 6
32 226 1 0 6 1 7
33 259 1 0 7 1 -2
34 289 1 0 0 1 -2
35 103 1 0 14 1 2
36 624 0 0 -2 0 3
37 68 1 0 -5 0 0
38 57 1 1 0 1 4
39 65 1 0 -6 0 -2
40 79 1 0 -3 0 -2
41 559 0 0 0 0 -1
42 79 1 1 4 1 -3
43 87 1 1 3 1 -1
44 91 1 1 4 1 3
45 297 1 1 6 1 -2
46 45 1 1 -3 1 -3
47 246 1 1 5 1 -2
48 37 1 1 -6 1 -3
49 37 1 1 -2 0 -1
50 538 0 1 16 1 5
51 541 0 1 -4 1 -2
52 184 1 1 12 1 22
53 122 1 1 0 0 -3
55 156 1 1 0 0 0
56 121 1 0 -3 1 -1
57 231 1 0 8 1 3
58 111 1 0 18 1 7
59 38 1 0 6 1 7
60 15 1 0 -2 0 4
61 54 1 0 1 1 2
62 127 1 0 -2 1 2
63 105 1 0 -2 0 -2
64 11 1 0 -4 0 -1
65 153 1 0 4 1 3
66 11 1 0 -4 1 2
67 79 1 0 2 1 3
68 46 1 0 1 1 2
69 655 0 0 11 1 -3
70 166 1 0 0 1 -3
72 95 1 0 9 1 2
73 83 1 0 13 1 9
74 151 1 0 3 1 9
75 220 1 0 3 1 3
76 227 1 0 -1 0 -3
77 343 1 0 6 1 2
78 119 1 0 -4 1 0
79 43 1 0 7 1 10
80 545 0 0 12 1 5
81 47 1 0 -1 1 5
82 15 1 0 4 1 0
83 805 0 0 0 0 -3
84 321 1 0 5 1 -1
85 167 1 0 3 0 -2
86 107 1 0 0 1 1
87 491 1 0 6 1 -2
88 35 1 0 -2 1 -1
89 123 1 0 1 1 9
90 597 0 0 -7 1 -1
91 762 0 0 3 1 -2
92 13 1 0 -4 1 2
93 31 1 0 7 1 1
94 228 1 1 -5 0 -2
95 553 0 1 26 1 6
96 190 1 1 -7 1 -2
97 307 1 1 -4 0 -2
98 73 1 1 -4 1 -2
99 208 1 1 -7 0 -2
100 267 1 1 -2 1 -1
102 169 1 1 0 1 12
103 125 1 1 2 0 -1
104 655 0 1 -5 1 2
105 70 1 1 3 1 2
106 398 1 1 -8 0 0
107 59 1 1 -2 1 -2
108 122 1 1 -5 1 5
111 96 1 0 8 0 -2
112 1172 0 1 5 0 0
113 734 0 0 5 0 -2
114 26 1 0 -5 1 -2
115 84 1 0 3 0 0
116 171 1 0 0 0 -1
117 159 1 0 15 1 -2
119 7 1 0 12 1 17
120 763 0 0 -1 0 -2
121 104 1 0 -6 0 -3
122 162 1 0 3 1 1
123 90 1 0 2 0 -3
124 373 1 0 -4 0 -3
125 115 1 0 -2 1 0
127 30 1 0 7 1 -2
128 8 1 0 -7 0 3
129 168 1 0 10 1 0
130 70 1 0 6 1 -3
131 130 1 0 -7 1 -1
132 285 1 0 5 0 -2
133 569 0 1 -5 1 -2
134 87 1 1 5 1 3
135 310 1 1 16 1 -3
136 87 1 1 2 0 0
137 544 0 1 5 0 13
138 156 1 1 4 1 -2
139 658 0 1 13 0 0
140 273 1 1 9 1 12
141 168 1 1 -3 1 -1
142 83 1 1 8 1 -2
143 4 1 1 7 1 4
144 708 0 1 14 1 1
145 137 1 1 -5 0 -2
146 259 1 1 4 0 -2
147 560 0 1 4 1 -1
148 586 0 1 8 1 -2
149 190 1 1 -6 1 -1
151 544 0 1 12 1 0
153 494 1 0 4 1 -2
154 541 0 0 15 1 5
155 94 1 0 10 1 1
156 567 0 0 -3 0 0
157 55 1 0 -6 0 0
158 93 1 0 -3 1 0
159 276 1 0 4 0 1
160 46 1 0 6 1 3
162 250 1 0 1 0 -2
163 106 1 0 10 1 -1
164 552 0 0 10 1 1
165 90 1 0 7 1 2
166 203 1 0 -5 1 3
167 67 1 1 11 1 0
168 559 1 1 -8 0 -2
169 106 1 1 1 1 5
170 374 1 1 -1 0 -2
171 630 0 1 -3 0 -1
172 61 1 1 -8 1 0
174 547 0 1 7 1 2
175 568 0 1 6 0 -1
176 490 1 1 -6 0 -2
177 222 1 1 -2 0 -3
178 56 1 1 -6 0 -2
179 282 1 1 -2 0 -1
180 35 1 1 -6 0 17
181 603 0 1 -6 0 -3
183 148 1 1 -1 0 -2
184 354 1 1 7 1 2
185 164 1 1 -4 0 -1
186 94 1 1 -1 1 -2
187 65 1 1 -1 1 1
188 567 0 1 3 0 -2
189 634 0 1 -1 1 -1
190 633 0 1 2 0 -1
191 127 1 1 2 0 1
192 477 1 1 3 0 5
193 436 1 1 -4 1 -1
195 362 1 1 -6 1 1
196 552 0 1 16 1 -1
197 144 1 1 -7 0 -1
198 242 1 1 10 1 5
199 564 0 1 18 0 1
200 299 1 1 3 0 -3
201 167 1 1 -9 0 4
202 380 1 1 8 0 -1
203 120 1 1 -2 1 1
204 248 1 0 0 1 3
205 218 1 1 9 1 3
206 115 1 0 7 1 -2
207 224 1 0 2 0 1
208 132 1 0 9 1 -1
209 148 1 0 -2 1 7
210 593 0 0 -4 0 -3
211 26 1 0 1 1 -3
213 32 1 0 4 1 5
214 292 1 0 -4 1 -1
215 89 1 0 -1 0 0
216 21 1 0 3 1 4
217 364 1 0 -5 0 5
218 142 1 0 4 1 0
219 188 1 0 2 0 -1
221 92 1 0 8 1 -3
222 56 1 0 2 1 5
223 110 1 0 1 1 4
224 555 0 0 10 0 0
225 220 1 0 -2 0 2
226 23 1 0 10 1 0
227 285 1 0 2 1 0
228 90 1 0 -1 1 2
229 59 1 0 -5 0 1
230 156 1 0 2 1 -1
231 194 1 0 6 1 -1
232 142 1 0 7 1 -1
233 57 1 0 8 1 5
234 279 1 0 1 1 6
235 118 1 0 0 1 0
236 567 0 0 4 0 1
237 562 0 1 3 1 1
238 239 1 1 6 1 7
239 578 0 1 2 0 -3
240 551 0 1 -1 1 -1
241 313 1 1 2 0 1
242 560 0 1 4 0 4
243 54 1 1 -4 1 28
244 198 1 1 -5 1 2
245 164 1 1 0 0 -1
246 325 1 1 3 1 3
247 62 1 1 10 1 3
248 45 1 1 -4 0 9
249 53 1 1 -4 1 2
250 253 1 1 5 1 1
251 51 1 1 -4 1 8
252 540 0 1 3 0 0
253 317 1 1 -3 1 1
254 437 1 1 -1 1 0
255 136 1 1 -1 1 6
256 115 1 1 9 1 4
257 175 1 1 13 1 -1
258 442 1 1 5 1 0
259 122 1 1 -4 0 -1
260 181 1 1 -3 0 0
261 180 1 1 -2 0 -1
262 51 1 1 0 0 1
263 541 0 1 1 1 -2
264 121 1 1 -4 1 13
265 328 1 1 6 1 1
267 166 1 1 -5 1 0
268 556 0 1 -4 0 -3
269 104 1 0 0 1 2
270 102 1 0 -2 0 2
272 144 1 0 4 0 -3
273 545 0 0 1 0 -1
274 537 0 0 0 1 -2
275 625 0 0 5 1 2
276 6 1 0 0 1 0
277 307 1 0 7 1 4
278 290 1 0 -1 0 9
279 20 1 0 -7 0 5
280 74 1 0 -7 0 1
281 100 1 0 14 0 -3
282 555 0 0 13 1 5
283 152 1 0 8 1 0
284 115 1 0 3 0 0
285 92 1 0 6 1 6
286 554 0 0 -4 1 -1
287 92 1 0 0 0 -3
288 69 1 0 4 0 -3
289 25 1 0 2 1 3
290 501 0 0 0 0 -2
291 86 1 0 7 1 7
292 99 1 0 5 1 4
293 87 1 0 0 0 -3
294 136 1 0 4 0 3
295 106 1 0 10 1 3
296 220 1 0 -2 1 0
297 36 1 0 2 0 3
298 162 1 0 15 1 -1
299 116 1 1 -6 0 -3
300 175 1 1 13 1 2
301 209 1 1 8 1 -3
302 545 0 1 3 1 -2
303 245 1 1 -4 1 -1
304 176 1 1 10 1 0
305 14 1 1 -3 1 -3
306 113 1 1 9 1 9
307 159 1 1 -1 0 -1
308 354 1 1 -1 1 0
309 174 1 1 -2 1 1
310 23 1 0 7 1 17
311 26 1 0 7 1 17
312 98 1 0 10 1 6
313 23 1 0 -5 1 2
314 555 0 0 10 0 -1
315 290 1 0 10 1 1
316 543 0 0 4 1 5
317 274 1 0 11 1 4
321 119 1 1 -7 0 -2
322 164 1 1 6 0 0
323 548 0 1 -2 0 -1
324 175 1 1 -7 0 0
325 539 0 1 -3 0 0
326 155 1 1 2 1 -3
327 14 1 1 8 1 12
328 187 1 1 8 0 3
329 65 1 1 15 1 5
330 159 1 1 -4 1 -2
331 96 1 1 -1 1 32
332 243 1 1 3 0 0
333 85 1 1 -5 0 -3
334 4 1 1 6 0 -1
335 121 1 1 7 1 1
336 659 1 1 -1 0 -3
337 260 1 1 -1 0 0
338 621 0 1 5 1 -2
339 199 1 1 3 0 -2
340 565 0 1 1 1 -1
341 183 1 1 7 1 2
342 122 1 1 2 1 1
343 170 1 1 3 1 10
344 15 1 1 6 0 11
345 268 1 1 -4 0 2
346 79 1 1 5 0 -3
347 23 1 0 -5 1 3
348 100 1 0 3 1 27
349 98 1 0 6 1 5
350 81 1 0 8 1 3
351 546 0 0 6 1 0
352 58 1 0 6 1 7
353 569 0 0 5 1 3
354 575 0 0 5 0 0
356 91 1 0 9 1 1
357 57 1 0 7 1 3
358 499 1 0 0 1 2
359 123 1 0 -4 1 4
360 143 1 0 9 0 2
362 471 1 0 2 0 1
363 74 1 0 -4 0 1
364 85 1 0 3 1 3
365 95 1 0 6 1 2
366 36 1 1 12 1 0
367 19 1 1 7 1 19
368 38 1 1 7 1 -2
369 539 0 1 2 0 1
370 567 0 1 -7 0 0
371 186 1 1 -9 0 7
372 546 0 1 -7 0 -2
373 24 1 1 4 1 -2
374 540 0 1 3 1 0
375 157 1 1 3 0 1
376 86 1 1 -4 1 -3
377 231 1 1 -2 0 -1
379 14 1 0 -3 1 0
380 75 1 0 -8 1 -1
381 147 1 0 1 1 3
382 105 1 0 -1 0 1
383 324 1 0 14 1 9
384 538 0 0 -4 0 -3
385 300 1 0 14 1 13
386 73 1 0 4 1 -2
387 65 1 0 6 1 3
388 568 1 0 13 1 17
389 84 1 0 7 0 -2
390 22 1 1 14 1 14
391 44 1 1 1 1 27
392 7 1 1 -6 0 0
394 21 1 1 7 1 1
395 537 0 1 11 1 9
396 186 1 1 2 0 0
397 40 1 1 -7 0 -1
398 287 1 1 3 1 0
399 538 0 1 13 0 6
400 30 1 1 3 1 5
401 516 1 1 11 1 0
402 268 1 1 11 1 -1
403 568 0 1 7 1 0
404 131 1 0 -4 0 0
405 399 1 0 3 1 3
406 78 1 0 0 0 -1
407 80 1 0 3 0 3
408 102 1 0 3 1 0
410 124 1 0 7 0 3
411 80 1 0 14 1 17
412 23 1 0 -10 0 -2
413 274 1 0 3 1 1
415 459 1 0 6 1 0
416 10 1 0 4 1 0
417 176 1 1 5 1 14
418 332 1 1 8 1 23
419 119 1 1 -6 0 0
420 217 1 1 -6 0 1
421 285 1 1 2 0 1
422 576 0 1 5 1 0
423 106 1 1 3 1 2
424 81 1 1 -1 1 1
425 47 1 1 12 1 27
426 76 1 1 -7 0 -2
427 348 1 1 -2 1 13
428 20 1 1 9 0 2
429 306 1 1 13 1 1
430 192 1 0 3 0 -3
431 216 1 0 7 1 17
432 189 1 0 19 1 4
434 193 1 0 6 0 -2
435 28 1 0 -3 1 10
436 150 1 0 5 1 -2
437 99 1 0 -5 1 0
438 510 0 0 3 1 0
439 306 1 0 4 1 4
440 101 1 0 8 1 7
441 102 1 0 6 1 -3
442 510 0 0 -3 1 -2
444 503 0 0 10 1 -3
445 52 1 0 2 1 0
446 547 0 0 8 1 -2
447 168 1 0 8 1 3
448 461 1 0 -7 0 -3
449 538 0 0 -4 1 -1
450 349 1 0 6 1 4
451 44 1 0 0 0 2
452 548 0 1 1 1 0
453 12 1 1 -7 0 -2
454 6 1 1 13 1 -3
455 575 0 1 -6 0 -2
456 589 0 1 12 0 -2
457 408 1 1 5 1 -2
458 232 1 1 -9 0 -3
459 143 1 1 15 1 -3
460 582 0 1 13 1 3
461 134 1 1 -6 0 -3
462 7 1 1 7 1 -2
463 548 0 1 10 1 -3
464 81 1 0 -3 1 -1
465 170 1 0 -1 1 -2
466 29 1 0 4 1 3
467 78 1 0 -1 1 17
468 81 1 0 5 1 2
469 369 1 1 9 1 1
470 69 1 1 11 1 -3
471 115 1 1 7 0 3
472 361 1 1 0 1 4
473 245 1 1 1 0 -3
474 233 1 0 10 1 5
475 227 1 0 2 0 -3
476 97 1 0 2 1 0
477 547 0 0 -3 0 -3
478 224 1 0 -7 0 -3
479 211 1 0 -7 0 -1
480 220 1 0 13 1 9
481 54 1 0 -6 0 -3
482 192 1 0 6 0 -1
483 138 1 0 -1 1 -2
484 107 1 1 9 1 -2
485 597 0 1 -7 0 -3
486 226 1 1 -3 1 1
487 434 1 1 -4 1 2
488 106 1 1 -4 0 -2
489 180 1 1 -5 0 -3
490 557 0 1 4 0 -3
491 556 0 1 15 1 -1
492 619 0 0 -7 0 -2
493 546 0 0 -4 0 -3
495 233 1 0 -6 1 -1
496 102 1 0 0 1 -1
497 548 0 0 -8 0 -3
498 99 1 0 -5 0 -2
499 36 1 0 0 1 37
501 78 1 1 3 0 -3
502 502 1 1 4 1 5
503 71 1 1 -1 0 -1
504 59 1 1 9 1 3
505 115 1 1 3 0 -2
506 533 0 1 -4 0 1
507 10 1 1 2 1 3
508 274 1 0 12 1 -1
509 255 1 0 9 1 5
510 503 0 0 6 0 -3
511 256 1 0 -8 0 -2
512 9 1 0 -3 0 -2
514 386 1 0 -2 0 -3
515 547 0 1 6 1 -2
516 45 1 1 1 0 0
517 58 1 1 -3 1 1
518 124 1 1 -7 0 -2
519 540 0 0 -6 1 17
520 243 1 0 8 1 -2
521 549 0 1 -5 0 -2
522 12 1 0 -4 0 -3
523 51 1 0 -8 0 -1
524 562 0 1 3 0 -1
525 94 1 1 0 1 4
526 204 1 0 15 1 0
527 238 1 0 -6 1 -3
528 140 1 1 -3 0 -3
529 120 1 1 6 1 7
530 154 1 0 8 1 -1
531 177 1 1 1 1 -1
532 119 1 1 10 1 4
533 83 1 1 -8 0 -3
534 130 1 1 1 0 -3
535 11 1 1 -7 1 17
536 159 1 0 9 1 0
537 211 1 0 -7 0 -2
538 33 1 0 3 0 -2
539 72 1 1 -3 1 -1
540 161 1 1 7 0 -1
541 191 1 0 5 1 22
542 181 1 0 -5 0 2
543 546 0 1 -3 0 -2
544 540 0 0 4 0 -3
545 76 1 0 0 0 0
546 7 1 0 5 1 4
547 44 1 0 4 0 -3
548 103 1 0 -5 1 2
549 79 1 0 4 0 -2
550 339 1 0 -6 1 0
551 90 1 0 -6 0 -1
552 542 0 0 10 0 0
553 384 1 0 3 0 -2
554 255 1 1 8 1 -2
555 431 1 1 2 0 -3
556 587 0 0 3 0 -2
557 198 1 0 -2 0 -1
558 551 0 0 1 1 -1
559 110 1 1 1 1 1
560 541 0 1 3 0 -3
561 242 1 0 -6 0 8
562 537 0 0 -4 0 0
563 56 1 1 1 1 4
564 34 1 1 10 1 1
566 549 0 1 4 0 0
567 133 1 0 0 1 4
568 226 1 0 6 1 0
569 401 1 1 8 1 3
570 14 1 1 1 1 3
571 548 0 0 -3 1 -1
572 224 1 1 2 1 12
573 540 0 0 5 0 2
574 237 1 0 14 1 10
575 354 1 1 1 1 7
576 123 1 0 -2 1 17
577 170 1 0 10 1 -2
578 203 1 1 -5 0 7
579 360 1 1 2 1 3
580 139 1 0 -1 0 -1
581 215 1 1 10 1 2
582 129 1 1 -3 1 -3
583 396 1 1 -4 0 -2
584 547 0 1 4 0 1
585 547 0 0 19 1 -1
586 71 1 1 -9 1 -2
587 168 1 1 9 1 -1
588 228 1 1 3 1 1
589 551 0 1 2 0 -2
590 654 0 0 5 1 3
591 51 1 1 1 0 -3
592 548 0 0 -2 0 0
593 231 1 1 -3 1 2
594 280 1 1 -1 0 -2
595 184 1 0 0 0 17
596 86 1 0 -3 0 0
598 46 1 1 10 1 -2
599 200 1 1 7 0 -1
600 244 1 0 3 0 -3
601 182 1 0 -2 0 -1
602 296 1 1 10 1 5
603 24 1 0 18 0 -3
604 142 1 0 8 0 -2
605 120 1 0 5 0 -3
606 47 1 0 7 1 -3
607 519 1 1 -5 0 -3
608 248 1 1 -4 0 -1
609 31 1 0 0 1 0
610 567 0 0 -2 1 -1
611 353 1 0 -7 1 1
612 458 1 1 6 0 -2
613 554 0 1 6 1 1
614 116 1 0 -6 0 -3
615 74 1 0 3 1 2
616 10 1 0 8 1 3
617 355 1 1 11 1 18
618 232 1 1 1 0 -3
619 68 1 1 1 1 8
620 48 1 1 7 0 -3
621 60 1 0 7 1 1
622 50 1 0 3 0 -3
624 126 1 0 23 1 3
625 18 1 0 7 1 1
626 35 1 1 -2 1 0
627 379 1 0 5 1 -1
628 377 1 1 16 1 12
;
*In the following PROC PHREG run, three SAS data sets are created:
1) Est1 contains the regression coefficient for the variable Treat
2) Outp contains the variable Treat and risk-score variable xbeta
for each input observations
3) Outb contains the estimated survival at the mean;
proc phreg data=uis outest=Est1(keep=TREAT);
model TIME*CENSOR(0)=TREAT AGE_C DRUG NDRGTX_C;
output out=Outp(keep=TREAT xbeta)xbeta=xbeta ;
baseline out=outb xbeta=xbeta survival=survival;
run;
*-----------------------------------------------------------------
Hosmer and Lemeshow (1999, p.145) suggest subtracting out the
contribution of the grouping variable from the risk score,
calculating the median value of what remains, and then adding
back in the contribution of the grouping variable when
calculating the estimator of the survival function.
----------------------------------------------------------------;
*Compute the risk scores for the remaining covariates;
data out1;
if _n_=1 then set est1(rename=(TREAT=b1));
set outp;
xbeta= xbeta - TREAT*b1;
run;
*Compute the median of the risk scores;
proc means data=Out1 noprint;
var xbeta;
output out=rm50(keep=rm50) median=rm50;
run;
*Compute the risk score-adjusted survival for Treat=0 and treat=1;
data Hosmer(keep=Group Time Survival);
if _n_=1 then do; set rm50; set est1(rename=(TREAT=b1)); end;
set outb(in=in0) outb(in=in1);
if in0 then do;
Survival= Survival ** exp(rm50 - xbeta);
Group='Treat=0';
end;
else do;
Survival= Survival ** exp(rm50 + b1 - xbeta);
Group='Treat=1';
end;
run;
*Plotting the risk score-adjusted survival curves;
title 'Risk Score-Adjusted Survival Curves';
proc sgplot data=Hosmer;
yaxis values=(0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1.0)
label="Survival Probability";
step y=Survival x=Time / group=Group
name="Survival";
keylegend "Survival" / location=outside;
run;
*-----------------------------------------------------------------
Maurbini and Valsecchi (1995, Chapter 6) advocates the direct-
adjusted survival curve of Makuch (1982), which is obtained by
averaging the individual estimated survivor curves over all
subjects at each survival time.
----------------------------------------------------------------;
data Out0(keep=xbeta) Out1(keep=xbeta);
set Outp;
if Treat=0 then output Out0;
else output Out1;
run;
*Average the individual survival curves in Treat=0;
data s0(keep=Time s);
retain s 0;
set outb(rename=(xbeta=xb)) end=eof;
do i=1 to n;
set Out0 point=i nobs=n;
s= s + Survival ** exp(xbeta-xb);
if i=n then s=s/n;
end;
run;
*Average the individual survival curves in Treat=1;
data s1(keep=Time s);
retain s 0;
set outb(rename=(xbeta=xb)) end=eof;
do i=1 to n;
set out1 point=i nobs=n;
s= s + Survival ** exp(xbeta-xb);
if i=n then s=s/n;
end;
run;
*Concatenate the two direct-adjusted survival curves in one
data set;
data Direct(rename=(s=Survival));
set s0 (in=in0) s1;
if in0 then Group='Treat=0';
else Group='Treat=1';
run;
*Plotting the direct-adjusted survival curves;
title 'Direct Covariate-Adjusted Survival Curves';
proc sgplot data=Direct;
yaxis values=(0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1.0)
label="Survival Probability";
step y=Survival x=Time / group=Group
name="Survival";
keylegend "Survival" / location=outside;
run;
*-----------------------------------------------------------------
Plotting the risk score-adjusted survival curves and the direct-
adjusted survival curve on the same graph.
----------------------------------------------------------------;
data both;
length Group $ 25;
set Hosmer(in=HL) Direct;
if HL then do;
if Group='Treat=0' then Group='Risk-Adjusted: Treat=0';
else Group='Risk-Adjusted: Treat=1';
end;
else do;
if Group='Treat=0' then Group='Direct-Adjusted: Treat=0';
else Group='Direct-Adjusted: Treat=1';
end;
run;
title 'Covariate-Adjusted Survival Curves';
proc sgplot data=Both;
yaxis values=(0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1.0)
label="Survival Probability";
step y=Survival x=Time / group=Group
name="Survival";
keylegend "Survival" / location=outside;
run;