Using a Time-Dependent Predictor in Complex Samples Cox Regression

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 on a sample drawn by complex sampling methods, but they are worried the proportional hazards assumption is invalid across age categories.

Persons released from their first arrest during the month of June 2003 were selected from sampled departments, and their case history inspected through the end of June 2006. The sample is collected in recidivism_cs_sample.sav. The sampling plan used is contained in recidivism_cs.csplan; because it makes use of a probability-proportional-to-size (PPS) method, there is also a file containing the joint selection probabilities (recidivism_cs_jointprob.sav). See the topic Sample Files for more information. Use Complex Samples Cox Regression to assess the validity of the proportional hazards assumption and fit a model with time-dependent predictors, if appropriate.

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