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Multilevel mixed-effects parametric survival analysis

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  • Michael J. Crowther

    (Centre for Biostatistics and Genetic Epidemiology, University of Leicester)

Abstract

Multilevel mixed-effects survival models are used in the analysis of clustered survival data, such as repeated events, multicenter clinical trials, or individual patient data meta-analyses, to investigate heterogeneity in baseline risk and treatment effects. I present the stmixed command for the parametric analysis of clustered survival data with two levels. Mixed-effects parametric survival models available include the exponential, Weibull and Gompertz proportional-hazards models, the Royston–Parmar flexible-parametric model, and the log–logistic, log–normal, and generalized gamma-accelerated failure-time models. Estimation is conducted using maximum likelihood, with both adaptive and nonadaptive Gauss–Hermite quadrature available. I will illustrate the command through simulation and application to clinical datasets.

Suggested Citation

  • Michael J. Crowther, 2013. "Multilevel mixed-effects parametric survival analysis," United Kingdom Stata Users' Group Meetings 2013 05, Stata Users Group.
    • Handle: RePEc:boc:usug13:05
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    References listed on IDEAS

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    1. Patrick Royston & Paul C. Lambert, 2011. "Flexible Parametric Survival Analysis Using Stata: Beyond the Cox Model," Stata Press books, StataCorp LLC, number fpsaus, July.
    2. Crowther, Michael J. & Lambert, Paul C., 2013. "stgenreg: A Stata Package for General Parametric Survival Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 53(i12).
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