Multilevel mixed-effects parametric survival analysis
Michael J. Crowther
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Michael J. Crowther: Centre for Biostatistics and Genetic Epidemiology, University of Leicester
United Kingdom Stata Users' Group Meetings 2013 from Stata Users Group
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 Roystonâ€“Parmar flexible-parametric model, and the logâ€“logistic, logâ€“normal, and generalized gamma-accelerated failure-time models. Estimation is conducted using maximum likelihood, with both adaptive and nonadaptive Gaussâ€“Hermite quadrature available. I will illustrate the command through simulation and application to clinical datasets.
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Persistent link: https://EconPapers.repec.org/RePEc:boc:usug13:05
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