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Discrete-Time Survival Mixture Analysis

University of California, Los Angeles

Katherine Masyn

Johns Hopkins University; University of California, Los Angeles

This article proposes a general latent variable approach to discrete-time survival analysis of nonrepeatable events such as onset of drug use. It is shown how the survival analysis can be formulated as a generalized latent class analysis of event history indicators. The latent class analysis can use covariates and can be combined with the joint modeling of other outcomes such as repeated measures for a related process. It is shown that conventional discrete-time survival analysis corresponds to a single-class latent class analysis. Multiple-class extensions are proposed, including the special cases of a class of long-term survivors and classes defined by outcomes related to survival. The estimation uses a general latent variable framework, including both categorical and continuous latent variables and incorporated in the Mplus program. Estimation is carried out using maximum likelihood via the EM algorithm. Two examples serve as illustrations. The first example concerns recidivism after incarceration in a randomized field experiment. The second example concerns school removal related to the development of aggressive behavior in the classroom.

Key Words: event history • growth mixture modeling • latent classes • maximum likelihood

Journal of Educational and Behavioral Statistics, Vol. 30, No. 1, 27-58 (2005)
DOI: 10.3102/10769986030001027


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