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Discrete-Time Survival Mixture Analysis

Author

Listed:
  • Bengt Muthén
  • Katherine Masyn

Abstract

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.

Suggested Citation

  • Bengt Muthén & Katherine Masyn, 2005. "Discrete-Time Survival Mixture Analysis," Journal of Educational and Behavioral Statistics, , vol. 30(1), pages 27-58, March.
    • Handle: RePEc:sae:jedbes:v:30:y:2005:i:1:p:27-58
    DOI: 10.3102/10769986030001027
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    Cited by:

    1. Marjan Petreski & Nikica Mojsoska-Blazevski & Marcelo Bergolo, 2017. "Labor-Market Scars When Youth Unemployment is Extremely High: Evidence from Macedonia," Eastern European Economics, Taylor & Francis Journals, vol. 55(2), pages 168-196, March.
    2. Miller, Elizabeth A. & Paschall, Katherine W. & Azar, Sandra T., 2017. "Latent classes of older foster youth: Prospective associations with outcomes and exits from the foster care system during the transition to adulthood," Children and Youth Services Review, Elsevier, vol. 79(C), pages 495-505.
    3. Hee-Koung Joeng & Ming-Hui Chen & Sangwook Kang, 2016. "Proportional exponentiated link transformed hazards (ELTH) models for discrete time survival data with application," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 22(1), pages 38-62, January.
    4. Cristian Ionel Rotaru, 2014. "Transitioning Out of Unemployment: Analysis Using the ABS Longitudinal Labour Force Survey File," Australian Journal of Labour Economics (AJLE), Bankwest Curtin Economics Centre (BCEC), Curtin Business School, vol. 17(2), pages 111-137.

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