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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

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.

Date: 2013-09-16
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