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dichotomous outcomes such as reincarceration, rearrest, and drug relapse. Logistic regression
also allows the researcher to enter various categorical (e.g., specific treatment program inmate
was in) or continuous variables (e.g., level of motivation) into models as covariates.
Finally, survival analysis allows the researcher to examine outcomes such as recidivism in
richer detail. Instead of a dichotomous outcome, recidivism is conceptualized as dynamic behavior
that occurs along a continuum of time. It requires consideration of how much time has elapsed
between release fi-om prison and specific behavioral outcomes such as drug relapse or rearrest for
parole violation or a new offense. We are thus concerned not only with examination of inmate and
programmatic variables that predict who is left standing at the end of 1-3 years, but variables that
innuence early v. later relapse over time. This consideration is important in examining outcomes
e
related to drug abuse, since multiple relapses almost inevitably precede any long-term recovery,
especially for seriously addicted inmates. Survival analysis and Cox regression techniques allow
researchers to add precision to predicting critical outcomes such as drug relapse and recidivism.
Cox regression allows for entry as independent variables both categorical and continuous
variables, and both individual as well as programmatic variables.
Jones, 1996; ONDCP, 1996, 1999; Pearson and Lipton, 1999).
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This document is a research report submitted to the U.S. Department of Justice. This report has not
been published by the Department. Opinions or points of view expressed are those of the author(s)
and do not necessarily reflect the official position or policies of the U.S. Department of Justice.