Analysis of Multivariate Failure Time Data
/****************************************************************/
/* S A S S A M P L E L I B R A R Y */
/* */
/* NAME: phrmult */
/* TITLE: Analysis of Multivariate Failure Time Data */
/* PRODUCT: STAT */
/* SYSTEM: ALL */
/* KEYS: survival analysis, multivariate analysis, ODS */
/* graphics */
/* PROCS: PHREG */
/* DATA: */
/* */
/* SUPPORT: sasycs UPDATE: */
/* REF: PROC LIFTEST Documentation, Details Section, */
/* Analysis of Multivariate Failure Time Data sub- */
/* section */
/* MISC: */
/* */
/****************************************************************/
/*----------------------------------------------------------------
This sample program illustrates how to use PROC PHREG to analyze
various types of multivariate failure time data:
1) multiple events (ordered/unordered) data are analyzed by the
marginal approach of Wei, Lin, and Weissfeld (1989)
2) clustered data are analyzed by the marginal approach of Lee,
Wei, and Amato (1992)
3) recurrent events data, which are special cases of ordered
multiple events data, can also be analyzed using an intensity
model (Andersen and Gill, 1982), a rate/mean model (Pepe
M.S. and Cai, J. 1993; Lawless and Nadeau, 1995; Lin et. al,
2000), or the conditional models of Prentice, Williams, and
Peterson (1981).
References:
Andersen, P.K. and Gill, R.D. (1982). Cox's regression model
for counting processes: a large sample study. Ann. Statist.,
10, 1100-1120.
Lawless, J.F. and Nadeau, C. (1995). Some simple and robust
methods for the analysis of recurrent events. Technometrics,
158-169.
Lee, E.W., Wei, L.J., and Amato, D. (1992). Cox-type regression
analysis for large number of small groups of correlated
failure time observations. In Survival Analysis, State of
the Art, Eds, J.P. Klein and P.K. Goel, 237-247. Netherlands:
Kluwer Academic Publishers.
Lin, D.Y., Wei, L.J., Yang, I. and Ying, Z. (2000). Semi-
parametric regression for the mean and rate functions of
recurrent events. J. R. Statist. Soc., B, 62, 711-730.
Pepe, M.S. and Cai, J. (1993). Some graphical displays and
marginal regression analyses for recurrent failure times
and time dependent covariates. J. Am. Statist. Assoc. 88,
811-820.
Prentice, R.L., Williams, B.J., and Peterson, A.V. (1981).
On the Regression Analysis of Multivariate Failure Time
Data. Biometrika, 68, 373-379.
Wei, L.J., Lin, D.Y., and Weissfeld. L. (1989). Regression
analysis of multivariate incomplete failure time data by
modeling marginal distribution. J. Am. Statist. Assoc. 84,
1065-1071.
---------------------------------------------------------------*/
/*****************************************************************
MARGINAL COX MODELS FOR MULTIPLE EVENTS DATA
AIDS Study. A randomized clinical trial was conducted to assess
the antiretrovial capability of ribavirin over times in AIDS
patients. Blood samples were collected at weeks 4, 8, and 12 from
each patient in three treatment groups (Placebo, Low dose, and
High dose) of ribavirin. For each serum sample, the failure time
is the number of days when virus positivity was detected. If a
sample was contaminated or when it took a longer period of time
than was achievable in the laboratory, the sample was censored.
Data Source: Wei, Lin, and Weissfeld (1989)
****************************************************************/
title 'AIDS Study';
data AIDS;
input trt week4 week8 week12;
label trt="1=Placebo,2=Low Dose,3=High Dose";
datalines;
1 9 6 7
1 4 5 10
1 6 7 6
1 10 . -21
1 15 8 .
1 3 . 6
1 4 7 3
1 9 12 12
1 9 19 -19
1 6 5 6
1 9 . 18
1 9 -20 -17
2 6 4 5
2 16 17 -21
2 31 -19 -21
2 -27 -19 .
2 7 16 -23
2 -28 7 -19
2 -28 3 16
2 15 12 16
2 18 -21 22
2 8 4 7
2 4 -21 7
3 21 9 -8
3 13 7 -21
3 16 6 20
3 3 8 6
3 21 . -25
3 7 19 3
3 11 13 -21
3 -27 -18 9
3 14 14 6
3 8 11 15
3 8 4 7
3 8 3 9
3 -19 10 -17
;
run;
data AIDS (drop= week4 week8 week12 i);
retain ID 0;
array w[3] week4 week8 week12;
set AIDS;
*Enum= 1 week 4 sample
2 week 8 sample
3 week 12 sample;
*Status= 1 event
0 censored;
ID + 1;
Z1= (trt=2);
Z2= (trt=3);
do i= 1 to 3;
if w[i] ^= . then do;
if w[i] < 0 then do;
Time= -w[i];
Status= 0;
end;
else do;
Time= w[i];
Status= 1;
end;
Enum= i;
Z11= Z1 * (Enum=1);
Z12= Z1 * (Enum=2);
Z13= Z1 * (Enum=3);
Z21= Z2 * (Enum=1);
Z22= Z2 * (Enum=2);
Z23= Z2 * (Enum=3);
output;
end;
end;
title2 'Unordered Multiple Events Data';
title3 'Marginal Cox Model with Noncommon Baseline Hazard Functions';
proc phreg data=AIDS outest=est2 covs(aggregate) covout;
model Time * Status(0) = Z11 Z12 Z13 Z21 Z22 Z23;
strata Enum;
id ID;
Z11= (trt=2) * (Enum=1);
Z12= (trt=2) * (Enum=2);
Z13= (trt=2) * (Enum=3);
Z21= (trt=3) * (Enum=1);
Z22= (trt=3) * (Enum=2);
Z23= (trt=3) * (Enum=3);
EqualLowDose: test Z11 - Z12, Z11 - Z13;
AverageLow: test Z11, Z12, Z13/e average;
EqualHighDose: test Z21 - Z22, Z21 - Z23;
AverageHigh: test Z21, Z22, Z23/e average;
run;
title2;
title3;
/******************************************************************
MARGINAL COX MODEL FOR CLUSTERED DATA
Tumor Study. One of the three female rats of the same litter
was subjected to a drug treatment. The failure time is the time
to tumor. If a rat died before the tumor was detected, the
failure time was censored.
Data Source: Mantel, M., Bohidar, N.R., and Ciminera, J. (1977).
Mantel-Haenszel analysis of litter-matched time-to response
data, with modifications for recovery of interlitter
information. Cancer Research, 37, 3863-3868. (Only the female
data were used since almost all male data were censored.)
******************************************************************/
title 'Tumor Study';
data Rats;
input Litter Treatment Time Status;
label Treatment='1=treated 0=placebo'
Time= 'weeks to tumor or death'
Status= '1=tumor 0=death';
datalines;
1 1 101 0
1 0 49 1
1 0 104 0
3 1 104 0
3 0 102 0
3 0 104 0
5 1 104 0
5 0 104 0
5 0 104 0
7 1 77 0
7 0 97 0
7 0 79 0
9 1 89 0
9 0 104 0
9 0 104 0
11 1 88 1
11 0 96 1
11 0 104 0
13 1 104 1
13 0 94 0
13 0 77 1
15 1 96 1
15 0 104 0
15 0 104 0
17 1 82 0
17 0 77 0
17 0 104 0
19 1 70 1
19 0 104 0
19 0 77 0
21 1 89 1
21 0 91 0
21 0 90 0
23 1 91 0
23 0 70 0
23 0 92 0
25 1 39 1
25 0 45 0
25 0 50 1
27 1 103 1
27 0 69 0
27 0 91 0
29 1 93 0
29 0 104 0
29 0 103 0
31 1 85 0
31 0 72 0
31 0 104 0
33 1 104 0
33 0 63 0
33 0 104 0
35 1 104 0
35 0 104 0
35 0 74 0
37 1 81 0
37 0 104 0
37 0 69 0
39 1 67 1
39 0 104 0
39 0 68 1
41 1 104 0
41 0 104 0
41 0 104 0
43 1 104 0
43 0 104 0
43 0 104 0
45 1 104 0
45 0 83 0
45 0 40 1
47 1 87 0
47 0 104 0
47 0 104 0
49 1 104 0
49 0 104 0
49 0 104 0
51 1 89 0
51 0 104 0
51 0 104 0
53 1 78 0
53 0 104 0
53 0 104 0
55 1 104 0
55 0 81 1
55 0 64 1
57 1 86 1
57 0 55 1
57 0 94 0
59 1 34 1
59 0 104 0
59 0 54 1
61 1 76 0
61 0 87 0
61 0 74 0
63 1 103 1
63 0 73 1
63 0 84 1
65 1 102 1
65 0 104 0
65 0 80 0
67 1 80 1
67 0 104 0
67 0 73 0
69 1 45 1
69 0 79 0
69 0 104 0
71 1 94 1
71 0 104 0
71 0 104 0
73 1 104 0
73 0 104 0
73 0 104 0
75 1 104 0
75 0 101 1
75 0 94 0
77 1 76 0
77 0 84 1
77 0 78 1
79 1 80 1
79 0 81 1
79 0 76 0
81 1 72 1
81 0 95 0
81 0 104 0
83 1 73 1
83 0 104 0
83 0 66 1
85 1 92 1
85 0 104 0
85 0 102 1
87 1 104 0
87 0 98 0
87 0 73 0
89 1 55 0
89 0 104 0
89 0 104 0
91 1 49 0
91 0 83 0
91 0 77 0
93 1 89 1
93 0 104 0
93 0 104 0
95 1 88 0
95 0 79 0
95 0 99 0
97 1 103 1
97 0 91 0
97 0 104 0
99 1 104 0
99 0 104 0
99 0 79 1
;
run;
title2 'Clustered Data';
title3 'Marginal Cox Model with a Common Baseline Hazard Function';
proc phreg data=Rats covs(aggregate);
model Time*Status(0) = Treatment;
id Litter;
run;
title2;
title3;
/*****************************************************************
RECURRENT EVENTS DATA
CGD Study. Chronic Granulomatous Disease (CGD) is a rare immune
disorder characterized by recurrent pyrogenic infections. A
placeco controlled randomized clinical trial was conducted by
the International CGD Cooperative Study Group to study the effect
of gamma interferon to reduce the rate of infections.
Data Source: Fleming, T.R. and Harrington, D.P. (1991). Counting
Process and Survival Data. New York: Wiley.
****************************************************************/
title 'CGD Study';
title 'Recurrent Events Data';
data CGD;
input ID TStart TStop Status Enum Trt Age;
label ID='Case Identification Number'
TStart='start time'
TStop='elased time from randomization'
Status='1=uncensored 0=censored'
Enum='recurrence sequence number';
datalines;
174054 0 293 0 1 1 38
174077 0 255 0 1 2 14
174109 0 213 0 1 2 26
174111 0 203 0 1 2 26
204001 0 219 1 1 1 12
204001 219 373 1 2 1 12
204001 373 414 0 3 1 12
204002 0 8 1 1 2 15
204002 8 26 1 2 2 15
204002 26 152 1 3 2 15
204002 152 241 1 4 2 15
204002 241 249 1 5 2 15
204002 249 322 1 6 2 15
204002 322 350 1 7 2 15
204002 350 439 0 8 2 15
204003 0 382 0 1 1 19
204004 0 388 0 1 1 12
204014 0 211 1 1 2 1
204014 211 260 1 2 2 1
204014 260 265 1 3 2 1
204014 265 269 1 4 2 1
204014 269 307 1 5 2 1
204014 307 363 0 6 2 1
204015 0 82 1 1 1 9
204015 82 114 1 2 1 9
204015 114 337 1 3 1 9
204015 337 367 0 4 1 9
204018 0 18 1 1 2 1
204018 18 362 0 2 2 1
204029 0 267 1 1 1 5
204029 267 360 0 2 1 5
204056 0 337 0 1 1 22
204085 0 270 0 1 2 19
204088 0 274 0 1 2 7
204102 0 271 0 1 1 25
204103 0 252 0 1 2 31
204131 0 243 0 1 1 37
204134 0 104 1 1 2 6
204134 104 227 0 2 2 6
204135 0 227 0 1 2 3
222121 0 198 0 1 2 22
222122 0 207 0 1 2 17
222123 0 168 1 1 2 19
222123 168 200 0 2 2 19
222125 0 197 0 1 1 36
238005 0 246 1 1 2 17
238005 246 253 1 2 2 17
238005 253 383 0 3 2 17
238009 0 294 1 1 2 27
238009 294 349 0 2 2 27
238010 0 371 0 1 1 5
238011 0 19 1 1 2 2
238011 19 102 0 2 2 2
238012 0 373 1 1 1 8
238012 373 388 0 2 1 8
238013 0 388 0 1 1 12
238016 0 365 0 1 2 27
238017 0 334 1 1 2 14
238017 334 370 1 2 2 14
238017 370 382 0 3 2 14
238019 0 373 0 1 1 11
238020 0 280 1 1 2 29
238020 280 385 0 2 2 29
238021 0 376 0 1 1 31
238022 0 360 0 1 1 7
238023 0 306 0 1 2 26
238036 0 118 1 1 1 13
238036 118 240 1 2 1 13
238036 240 251 0 3 1 13
238044 0 187 1 1 1 25
238044 187 356 0 2 1 25
238045 0 339 0 1 1 9
238046 0 6 1 1 2 28
238046 6 301 0 2 2 28
238051 0 334 0 1 1 13
238065 0 269 0 1 1 24
238076 0 273 0 1 2 11
238086 0 113 1 1 1 4
238086 113 273 0 2 1 4
238087 0 99 1 1 2 19
238087 99 306 1 2 2 19
238099 0 263 0 1 1 18
238100 0 167 1 1 1 7
238100 167 240 0 2 1 7
238106 0 271 0 1 2 12
238107 0 254 0 1 1 10
242091 0 278 0 1 2 9
242094 0 265 0 1 1 5
242101 0 217 0 1 1 1
242110 0 165 1 1 1 7
242110 165 210 0 2 1 7
242117 0 192 0 1 2 11
242119 0 11 1 1 2 4
242119 11 22 1 2 2 4
242119 22 169 1 3 2 4
242119 169 195 0 4 2 4
242132 0 120 1 1 2 7
242132 120 203 0 2 2 7
242133 0 197 0 1 1 15
243030 0 206 1 1 2 1
243030 206 347 0 2 2 1
243040 0 335 0 1 1 7
243041 0 274 1 1 1 17
243041 274 361 1 2 1 17
243041 361 365 0 3 1 17
243042 0 336 0 1 1 8
243052 0 52 1 1 2 20
243052 52 65 1 2 2 20
243052 65 255 1 3 2 20
243052 255 270 0 4 2 20
243053 0 67 1 1 2 5
243053 67 248 1 2 2 5
243053 248 250 1 3 2 5
243053 250 284 1 4 2 5
243053 284 347 0 5 2 5
243060 0 318 0 1 1 6
243061 0 318 1 1 2 9
243061 318 359 0 2 2 9
243097 0 259 0 1 1 5
245006 0 364 0 1 1 44
245007 0 292 1 1 2 22
245007 292 364 0 2 2 22
245008 0 363 0 1 1 7
245043 0 350 0 1 1 19
248078 0 269 0 1 1 34
248108 0 185 0 1 1 32
248115 0 91 0 1 2 25
248116 0 91 0 1 2 21
249028 0 357 0 1 2 7
249031 0 175 1 1 2 5
249031 175 280 1 2 2 5
249031 280 353 0 3 2 5
249035 0 343 0 1 2 24
249055 0 265 1 1 1 12
249055 265 303 0 2 1 12
249063 0 226 1 1 2 5
249063 226 322 0 2 2 5
249105 0 255 0 1 1 24
328064 0 65 1 1 1 26
328064 65 343 0 2 1 26
328073 0 294 0 1 2 6
328074 0 303 0 1 1 9
328079 0 23 1 1 2 25
328079 23 270 0 2 2 25
328080 0 270 0 1 1 2
328083 0 245 0 1 2 8
328084 0 261 0 1 2 10
328092 0 284 0 1 1 20
328093 0 276 0 1 2 34
328095 0 294 0 1 1 6
328096 0 277 0 1 1 11
328098 0 4 1 1 2 3
328098 4 159 1 2 2 3
328098 159 213 1 3 2 3
328098 213 287 0 4 2 3
328104 0 331 0 1 1 9
328112 0 288 0 1 2 11
328113 0 269 0 1 2 17
328114 0 269 0 1 2 10
331047 0 330 0 1 2 17
331062 0 57 1 1 2 8
331062 57 121 1 2 2 8
331062 121 351 0 3 2 8
331069 0 297 0 1 1 6
331075 0 281 0 1 2 7
331081 0 276 0 1 1 7
331089 0 279 0 1 1 8
331090 0 14 1 1 2 11
331090 14 278 0 2 2 11
331118 0 199 0 1 1 4
332032 0 308 0 1 2 18
332033 0 327 0 1 1 13
332034 0 329 0 1 2 11
332037 0 318 0 1 1 2
332038 0 304 0 1 1 17
332039 0 316 0 1 2 35
332048 0 300 0 1 2 25
332049 0 146 1 1 1 14
332049 146 188 1 2 1 14
332049 188 300 0 3 1 14
332050 0 304 1 1 2 25
332050 304 312 0 2 2 25
332057 0 91 1 1 2 27
332057 91 121 1 2 2 27
332057 121 203 1 3 2 27
332057 203 287 0 4 2 27
332058 0 293 0 1 2 32
332059 0 293 0 1 1 6
332066 0 264 1 1 2 8
332066 264 286 0 2 2 8
332067 0 286 0 1 1 9
332068 0 273 0 1 1 23
332070 0 236 1 1 2 23
332070 236 273 0 2 2 23
332071 0 273 0 1 1 17
332072 0 207 1 1 1 21
332072 207 273 0 2 1 21
332082 0 264 0 1 2 1
336024 0 160 0 1 1 12
336025 0 146 1 1 2 7
336025 146 316 0 2 2 7
336026 0 316 0 1 2 1
336027 0 315 0 1 1 3
;
data Pattern;
Trt=1;
Age=30;
output;
Trt=2;
Age=1;
output;
run;
/*
Under the intensity model, the risk of a recurrent event for a
subject satisfies the usual proportional hazards model, and it
is unaffected by any earlier events that occurred to the same
subject unless covariates that captured such dependence are
explictly specified in the model. You can predict the
cumulative hazard function for a given pattern of covariate
values.
*/
title3 'Intensity Model';
proc phreg data=CGD;
model (TStart TStop)*Status(0)= Trt Age;
baseline covariates=Pattern out=Out1 cumhaz=_all_/nomeans;
run;
/*
The rate/mean model assumes that the underlying counting process
is a time-transformed Poisson process and the covariates have
multiplicative effects on the means and rate functions of the
counting process.
*/
title3 'Proportional Means Model';
proc phreg data=CGD covs(aggregate);
model (TStart TStop)*Status(0)= Trt Age;
id ID;
baseline covariates=Pattern out=Out2 cmf=_all_/nomeans;
run;
proc template;
define statgraph CMF;
dynamic _title;
BeginGraph;
entrytitle "Estimates of Cumulative Mean Frequencies of Infections";
layout gridded;
layout overlay /
xaxisopts=(label="Follow-up time (days)")
yaxisopts=(label="CMF"
linearopts=(tickvaluelist= (0 .2 .4 .6 .8 1.0 1.2 1.4
1.6 1.8 2.0 2.2 2.4 2.6)));
stepplot y=CMF x=TStop / group=Patient name="mean";
discretelegend "mean" / location=inside
hAlign=left
vAlign=top
across=1
border=true;
endlayout;
endlayout;
EndGraph;
end;
run;
ods graphics on;
ods html;
data _null_;
set out2;
if Trt=1 and Age=30 then Patient='Gamma Interferon, Age=30';
else Patient='Placebo, Age=1';
file print ods=(template="CMF");
put _ods_;
output;
run;
ods html close;
ods graphics off;
/*
The analysis of the PWP (Prentice, Williams, and Peterson)
conditional models is confined to the first three infections.
GapTime is the time between the current recurrence and the
prevous recurrence.
*/
data CGD2;
set CGD;
GapTime= TStop-TStart;
if Enum > 3 then delete;
run;
title3 'PWP total time model with stratum-specific regression coefficients';
proc phreg data=CGD2;
model (TStart,TStop)*Status(0)=Trt1 Trt2 Trt3 Age1 Age2 Age3;
strata Enum;
Trt1= Trt * (Enum=1);
Trt2= Trt * (Enum=2);
Trt3= Trt * (Enum=3);
Age1= Age * (Enum=1);
Age2= Age * (Enum=2);
Age3= Age * (Enum=3);
run;
title3 'PWP total time model with common regression coefficients';
proc phreg data=CGD2;
model (TStart,TStop)*Status(0)=Trt Age;
strata Enum;
run;
title3 'PWP gap time model with spratum-specific regression coefficients';
proc phreg data=CGD2;
model GapTime*Status(0)=Trt1 Trt2 Trt3 Age1 Age2 Age3;
strata Enum;
Trt1= Trt * (Enum=1);
Trt2= Trt * (Enum=2);
Trt3= Trt * (Enum=3);
Age1= Age * (Enum=1);
Age2= Age * (Enum=2);
Age3= Age * (Enum=3);
run;
title3 'PWP gap time model with common regression coefficients';
proc phreg data=CGD2;
model GapTime*Status(0)=Trt Age;
strata Enum;
run;
