Abstract
In this article, I present the community-contributed stmixed command for fitting multilevel survival models. It serves as both an alternative to Stata’s official mestreg command and a complimentary command 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) and user-defined hazard models. Simple or complex time-dependent effects can be included, as can expected mortality for a relative survival model. Left-truncation (delayed entry) is supported, and t-distributed random effects are provided as an alternative to Gaussian random effects. I illustrate the methods 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, Stata Journal 12: 674–687; 2013, Statistics in Medicine 32: 4118–4134). stmixed is part of the merlin family (Crowther 2017, arXiv Working Paper No. arXiv:1710.02223; 2018, arXiv Working Paper No. arXiv:1806.01615).
Highlights
Clustered survival data is often observed in a variety of settings
A 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 (Gutierrez, 2002)
In the field of meta-analysis, the individual patient data (IPD) meta-analysis of survival data is growing in use, as this form of analysis is recognised as the gold standard approach (Simmonds et al, 2005)
Summary
Clustered survival data is often observed in a variety of settings. Within medical research, a 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 (Gutierrez, 2002). Prevalent in cancer survival studies, relative survival allows the modelling of excess mortality associated with a diseased population compared to that of the general population (Dickman et al, 2004) Such data often exhibits a hierarchical structure, with patients nested within geographical regions such as counties. I illustrate the command in Section 5 with a dataset of patients with kidney disease who are followed up for recurrent infection at the catheter insertion point, and show how to simulate clustered survival data using the survsim command, representing an IPD meta-analysis scenario, with a random treatment effect.
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