Example 48.4 Analysis of Arbitrarily Censored Data with Interaction Effects

The artificial data in this example are from a study of the natural recovery time of mice after injection of a certain toxin. Twenty mice were grouped by sex (sex: 1 = Male, 2 = Female) with equal sizes. Their ages (in days) were recorded at the injection. Their recovery times (in minutes) were also recorded. Toxin density in blood was used to decide whether a mouse recovered. Mice were checked at two times for recovery. If a mouse had recovered at the first time, the observation is left censored, and no further measurement is made. The variable time1 is set to missing and time2 is set to the measurement time to indicate left censoring. If a mouse had not recovered at the first time, it was checked later at a second time. If it had recovered by the second measurement time, the observation is interval censored, and the variable time1 is set to the first measurement time and time2 is set to the second measurement time. If there was no recovery at the second measurement, the observation is right censored, and time1 is set to the second measurement time and time2 is set to missing to indicate right censoring.

The following statements create a SAS data set containing the data from the experiment:

   title 'Natural Recovery Time';
   data mice;
      input sex age time1 time2 ;
      datalines;
   1  57  631   631
   1  45  .     170
   1  54  227   227
   1  43  143   143
   1  64  916   .
   1  67  691   705
   1  44  100   100
   1  59  730   .
   1  47  365   365
   1  74  1916  1916
   2  79  1326  .
   2  75  837   837
   2  84  1200  1235
   2  54  .     365
   2  74  1255  1255
   2  71  1823  .
   2  65  537   637
   2  33  583   683
   2  77  955   .
   2  46  577   577
   ;

The following SAS statements create the SAS data sets xrow1 and xrow2:

   data xrow1;
      input sex age time1 time2 ;
      datalines;
   1  50  .  .
   ;
    
   data xrow2;
      input sex age time1 time2 ;
      datalines;
   2  60.6  .  .
   ;

The following SAS statements fit a Weibull model with age, sex, and an age-by-sex interaction term as covariates, and create a plot of predicted probabilities against recovery time for the fixed values of age and sex specified in the SAS data set xrow1:

   ods graphics on;
   proc lifereg data=mice xdata=xrow1;
      class sex  ;
      model (time1, time2) = age sex age*sex / dist=Weibull;
    
      probplot  / nodata
          plower=.5
          vref(intersect) = 75
          vreflab = '75 Percent'
          ;
      inset;
   run;

Standard output is shown in Output 48.4.1. Tables containing general model information, Type III tests for the main effects and interaction terms, and parameter estimates are created.