Michael J. Crowther
Associate Professor of Biostatistics
University of Leicester and Karolinska Institutet
Clustered survival data is often observed in a variety of settings. Patients treated, or living, in the same area may share unobserved characteristics (frailty), such as environmental aspects or medical care access.
Another common example is the analysis of recurrent event data, where individual patients can experience the event of interest multiple times throughout the follow-up period, and the inherent correlation within patients can be accounted for using a frailty term.
Crowther recently published the Stata program stmixed which can fit multilevel survival models with any number of levels and random effects at each level, including flexible spline-based approaches (such as Royston-Parmar and the log hazard equivalent) or user-defined hazard models.
Simple or complex time-dependent effects can be included, as well as the addition of expected mortality for relative survival models.
Statisticians and researchers with a good working knowledge of the principles and practice of survival analysis, including modelling of survival data.
• Provide an overview of multilevel mixed effects survival analysis
• Introduce and illustrate tools in Stata for conducting multilevel survival analysis, including both modelling and prediction, with a focus on calculating clinically useful predictions.
Lectures with time for questions and discussion. No computer labs, but practical exercises will be available in advance, and some solutions will be presented and discussed.
Crowther MJ. Multilevel mixed effects parametric survival analysis: Estimation, simulation and application. Stata Journal 2019;19(4):931-949. (Pre-print: https://arxiv.org/abs/1709.06633)
Crowther MJ, Look MP, Riley RD. Multilevel mixed effects parametric survival models using adaptive Gauss-Hermite quadrature with application to recurrent events and individual participant data meta-analysis. Statistics in Medicine 2014;33(22):3844-3858.