Chapter
12
Complex Samples Cox Regression
The Complex Samples Cox Regression procedure performs survival analysis for samples drawn
by complex samplingmethods. Optionally,you can request analyses for a subpopulation.
Examples. A government law enforcement agency is concerned about recidivism rates in their area
of jurisdiction. One of the measures of recidivism is the time until second arrest for offenders. The
agency would like to model time to rearrest using Cox Regression but are worried the proportional
hazards assumption is invalid across age categories.
Medical researchers are investigating survival times forpatients exiting a rehabilitation program
post-ischemic stroke. There is the potential for multiple cases per subject, since patient histories
change as the occurrence of significant nondeathevents are noted and the times of these events
recorded. The sample is also left-truncated in the sense that the observed survival times are
“inflated” by the length of rehabilitation, becausewhile the onsetof risk starts at the time of the
ischemic stroke, onlypatients who survive past the rehabilitation program are in the sample.
SurvivalTime. The procedure applies Cox regression to analysis of survival times—that is, the
length of time beforethe occurrence of an event. Thereare two ways to specify the survival time,
depending upon the start time of the interval:
Time=0. Commonly,you will have complete information on thestart of the interval fo r each
subject and will simply have a variable containingend times (or create a single variable with
end times from Date & Time variables; see below).
Variesby subject. This is appropriate when you have left-truncation, also called delayed
entry; for example, if you are analyzing survivaltimes for patients exiting a rehabilitation
programpost-stroke, you might consider that their onset of risk starts at the time of the stroke.
However,if your sample only includes patients who have survived the rehabilitation program,
then your sampleis left-truncated in the sense that the observed survival times are “inflated”
by the length of rehabilitation. You can accountfor this by specifying the time at which they
exited rehabilitation as thetime of entry into the study.
Date& Time Variables. Date & Time variables cannot be used to directly defineth e start and
end of the interval; if you have Date & Time variables, you should use them to create variables
containing survival times. If there is no left-truncation, simply create a variable containing end
times based uponthe difference between the date of entry into the study and the observation date.
If there is left-truncation, create a variable containing start times, based upon thedifference
betweenthe date of the start of the study and the date of entry, and a variable containing end times,
based upon thedifference between the date of the start of the study and the date of observation.
EventStatus. Youneed a variable that records whether the subject experienced the event of interest
within the interval. Subjects for whom the event has not occurred are right-censored.
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