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Multilevel mixed-effects parametric survival analysis: Estimation, simulation, and application
journal contribution
posted on 2020-03-26, 11:46 authored by MJ CrowtherIn 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).
Funding
MRC New Investigator Research Grant (MR/P015433/1)
History
Citation
Stata Journal, 2019, Vol. 9, Issue 4Author affiliation
Department of Health SciencesVersion
- AM (Accepted Manuscript)
Published in
Stata JournalVolume
19Issue
4Pagination
931 - 949Publisher
SAGE Publicationsissn
1536-867Xeissn
1536-8734Copyright date
2019Publisher DOI
Publisher version
https://journals.sagepub.com/doi/full/10.1177/1536867X19893639Language
enUsage metrics
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