The PHREG Procedure

Example 85.15 Analysis of Competing-Risks Data

Bone marrow transplant (BMT) is a standard treatment for acute leukemia. Klein and Moeschberger (1997) present a set of BMT data for 137 patients, grouped into three risk categories based on their status at the time of transplantation: acute lymphoblastic leukemia (ALL), acute myelocytic leukemia (AML) low-risk, and AML high-risk. During the follow-up period, some patients might relapse or some patients might die while in remission. Consider relapse to be the event of interest. Death is a competing risk because death impedes the occurrence of leukemia relapse. The Fine and Gray (1999) model is used to compare the risk categories on the disease-free survival.

The following DATA step creates the data set Bmt. The variable Disease represents the risk group of a patient, which is either ALL, AML-Low Risk, or AML-High Risk. The variable T represents the disease-free survival in days, which is the time to relapse, time to death, or censored. The variable Status has three values: 0 for censored observations, 1 for relapsed patients, and 2 for patients who die before experiencing a relapse.

proc format;
   value DiseaseGroup 1='ALL'
                      2='AML-Low Risk'
                      3='AML-High Risk';
                      
data Bmt;
   input Disease T Status @@;
   label T='Disease-Free Survival in Days';
   format Disease DiseaseGroup.;
   datalines;
1   2081   0   1   1602   0   1   1496   0   1   1462   0   1   1433   0
1   1377   0   1   1330   0   1    996   0   1    226   0   1   1199   0
1   1111   0   1    530   0   1   1182   0   1   1167   0   1    418   2
1    383   1   1    276   2   1    104   1   1    609   1   1    172   2
1    487   2   1    662   1   1    194   2   1    230   1   1    526   2
1    122   2   1    129   1   1     74   1   1    122   1   1     86   2
1    466   2   1    192   1   1    109   1   1     55   1   1      1   2
1    107   2   1    110   1   1    332   2   2   2569   0   2   2506   0
2   2409   0   2   2218   0   2   1857   0   2   1829   0   2   1562   0
2   1470   0   2   1363   0   2   1030   0   2    860   0   2   1258   0
2   2246   0   2   1870   0   2   1799   0   2   1709   0   2   1674   0
2   1568   0   2   1527   0   2   1324   0   2    957   0   2    932   0
2    847   0   2    848   0   2   1850   0   2   1843   0   2   1535   0
2   1447   0   2   1384   0   2    414   2   2   2204   2   2   1063   2
2    481   2   2    105   2   2    641   2   2    390   2   2    288   2
2    421   1   2     79   2   2    748   1   2    486   1   2     48   2
2    272   1   2   1074   2   2    381   1   2     10   2   2     53   2
2     80   2   2     35   2   2    248   1   2    704   2   2    211   1
2    219   1   2    606   1   3   2640   0   3   2430   0   3   2252   0
3   2140   0   3   2133   0   3   1238   0   3   1631   0   3   2024   0
3   1345   0   3   1136   0   3    845   0   3    422   1   3    162   2
3     84   1   3    100   1   3      2   2   3     47   1   3    242   1
3    456   1   3    268   1   3    318   2   3     32   1   3    467   1
3     47   1   3    390   1   3    183   2   3    105   2   3    115   1
3    164   2   3     93   1   3    120   1   3     80   2   3    677   2
3     64   1   3    168   2   3     74   2   3     16   2   3    157   1
3    625   1   3     48   1   3    273   1   3     63   2   3     76   1
3    113   1   3    363   2
;

PROC PHREG enables you to plot the cumulative incidence function for each disease category, but first you must save these three Disease values in a SAS data set, as in the following DATA step:

data Risk;
   Disease=1; output;
   Disease=2; output;
   Disease=3; output;
   format Disease DiseaseGroup.;
   run;

The following statements use the PHREG procedure to fit the proportional subdistribution hazards model. To designate relapse (Status=1) as the event of interest, you specify EVENTCODE=1 in the MODEL statement. The HAZARDRATIO statement provides the hazard ratios for all pairs of disease groups. The COVARIATES= option in the BASELINE statement specifies the data set that contains the covariate settings for predicting cumulative incidence functions; and the OUT= option saves the prediction results in a SAS data set. The PLOTS= option in the PROC PHREG statement displays the cumulative incidence curves.

ods graphics on;
proc phreg data=Bmt plots(overlay=stratum)=cif;
   class Disease (order=internal ref=first);
   model T*Status(0)=Disease / eventcode=1;
   Hazardratio 'Pairwise' Disease / diff=pairwise;
   baseline covariates=Risk out=out1 cif=_all_ / seed=191;
run;

Output 85.15.1 displays the codes of different types of observations in the input data set. Relapse is the failure of interest with Status = 1, death is a competing failure with Status = 2, and censored observations are those with Status = 0. Out of the 137 transplant patients, 42 have a relapse, 41 die without experiencing a relapse, and 54 are censored (Output 85.15.2).

Output 85.15.1: Code for the Competing Failures and Censored Observations

The PHREG Procedure

Model Information
Data Set WORK.BMT  
Dependent Variable T Disease-Free Survival in Days
Status Variable Status  
Event of Interest 1  
Competing Event 2  
Censored Value 0  



Output 85.15.2: Distribution of Events and Censored Observations

Summary of Failure Outcomes
Total Event of
Interest
Competing
Event
Censored
137 42 41 54



Output 85.15.3 shows a significant effect (p = 0.0030) of Disease on the disease-free survival. With the reference coding, the CLASS variable Disease is represented by two dummy variables. Parameter estimates and Wald tests for individual parameters are shown in Output 85.15.3.

Output 85.15.3: Wald Test of the Disease Effect

Type 3 Tests
Effect DF Wald Chi-Square Pr > ChiSq
Disease 2 11.6406 0.0030

Analysis of Maximum Likelihood Estimates
Parameter   DF Parameter
Estimate
Standard
Error
Chi-Square Pr > ChiSq Hazard
Ratio
Label
Disease AML-Low Risk 1 -0.80340 0.42846 3.5160 0.0608 0.448 Disease AML-Low Risk
Disease AML-High Risk 1 0.50849 0.36618 1.9283 0.1649 1.663 Disease AML-High Risk



Hazard ratio estimates of one disease group relative to another disease group are displayed in Output 85.15.4. The hazard of relapse for the ALL patients is 2.2 times that for the AML-low risk patients, and the hazard for the AML-high risk patients is 1.7 times that for the ALL patients. It is expected that at any given time after the transplant, an AML high-risk patient is more likely to relapse than an ALL patient, and an ALL patient is more likely to relapse than an AML low-risk patient. Such ordering of probabilities is revealed in the plot of the cumulative incidence functions in Output 85.15.5.

Output 85.15.4: Pairwise Comparison of Disease Group

Pairwise: Hazard Ratios for Disease
Description Point Estimate 95% Wald Confidence Limits
Disease ALL vs AML-Low Risk 2.233 0.964 5.171
Disease AML-Low Risk vs ALL 0.448 0.193 1.037
Disease ALL vs AML-High Risk 0.601 0.293 1.233
Disease AML-High Risk vs ALL 1.663 0.811 3.408
Disease AML-Low Risk vs AML-High Risk 0.269 0.127 0.573
Disease AML-High Risk vs AML-Low Risk 3.713 1.745 7.900



Output 85.15.5: CIF of the Three Disease Groups

CIF of the Three Disease Groups


You use the following statements to display the cumulative incidence prediction for the ALL (Disease=1) risk group:


proc print data=Out1(where=(Disease=1));
   title 'CIF Estimates and 95% Confidence limits for the ALL Group';
run;

Output 85.15.6: Cumulative Incidence Prediction

CIF Estimates and 95% Confidence limits for the ALL Group

Obs Disease T CIF StdErrCIF LowerCIF UpperCIF
1 ALL 0 0.00000 . . .
2 ALL 32 0.00727 0.007237 0.00103 0.05114
3 ALL 47 0.02183 0.014323 0.00604 0.07898
4 ALL 48 0.02922 0.017822 0.00884 0.09657
5 ALL 55 0.03663 0.019106 0.01318 0.10181
6 ALL 64 0.04405 0.019259 0.01870 0.10378
7 ALL 74 0.05151 0.019951 0.02411 0.11005
8 ALL 76 0.05897 0.025533 0.02524 0.13778
9 ALL 84 0.06646 0.025378 0.03145 0.14048
10 ALL 93 0.07400 0.025092 0.03807 0.14383
11 ALL 100 0.08158 0.030460 0.03924 0.16959
12 ALL 104 0.08920 0.029038 0.04712 0.16883
13 ALL 109 0.09682 0.033564 0.04907 0.19100
14 ALL 110 0.10443 0.035734 0.05341 0.20422
15 ALL 113 0.11205 0.041176 0.05453 0.23026
16 ALL 115 0.11972 0.037619 0.06467 0.22163
17 ALL 120 0.12742 0.036521 0.07266 0.22347
18 ALL 122 0.13518 0.042929 0.07254 0.25190
19 ALL 129 0.14293 0.041747 0.08063 0.25336
20 ALL 157 0.15068 0.046376 0.08243 0.27545
21 ALL 192 0.15848 0.051406 0.08392 0.29928
22 ALL 211 0.16628 0.058106 0.08383 0.32983
23 ALL 219 0.17404 0.056257 0.09236 0.32794
24 ALL 230 0.18185 0.053563 0.10210 0.32392
25 ALL 242 0.18967 0.065355 0.09653 0.37265
26 ALL 248 0.19753 0.057829 0.11128 0.35062
27 ALL 268 0.20535 0.054765 0.12176 0.34634
28 ALL 272 0.21322 0.058189 0.12489 0.36402
29 ALL 273 0.22105 0.061340 0.12832 0.38080
30 ALL 381 0.22893 0.061228 0.13554 0.38669
31 ALL 383 0.23677 0.062212 0.14147 0.39626
32 ALL 390 0.24461 0.063708 0.14682 0.40754
33 ALL 421 0.25250 0.070833 0.14571 0.43757
34 ALL 422 0.26035 0.063694 0.16118 0.42053
35 ALL 456 0.26825 0.067518 0.16379 0.43932
36 ALL 467 0.27621 0.073253 0.16424 0.46450
37 ALL 486 0.28422 0.066216 0.18004 0.44871
38 ALL 606 0.29233 0.067521 0.18590 0.45971
39 ALL 609 0.30039 0.079301 0.17905 0.50396
40 ALL 625 0.30845 0.067182 0.20128 0.47270
41 ALL 662 0.31657 0.070668 0.20439 0.49033
42 ALL 748 0.32469 0.082845 0.19692 0.53537



Output 85.15.6 shows the point estimate and the confidence limits for the cumulative incidence at each distinct time when the event of interest occurred for the ALL patients. The predictions for the AML-low risk patients and AML-high risk patients are not shown.