Multilevel mixed effects parametric survival analysis: Estimation, simulation and application
Abstract
In this article, I present the user written stmixed command for the fitting of multilevel survival models, which serves as both an alternative to Stata's official mestreg, and a complimentary program with substantial extensions. stmixed 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 a relative survival model. Left-truncation/delayed entry can be used and t-distributed random effects are provided as an alternative to Gaussian random effects. The methods are illustrated with a commonly used dataset of patients with kidney disease suffering recurrent infections, and a simulated example, illustrating a simple approach to simulating clustered survival data using survsim (Crowther and Lambert 2012, 2013). stmixed is part of the merlin family (Crowther 2017, 2018).
Keywords
Cite
@article{arxiv.1709.06633,
title = {Multilevel mixed effects parametric survival analysis: Estimation, simulation and application},
author = {Michael J. Crowther},
journal= {arXiv preprint arXiv:1709.06633},
year = {2020}
}