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.
