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Multilevel mixed-effects parametric survival analysis: Estimation, simulation, and application

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  • Michael J. Crowther

    (University of Leicester)

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 ex- ample illustrating a simple approach to simulating clustered survival data using survsim (Crowther and Lambert 2012, Stata Journal 12: 674–687; 2013, Statis- tics 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).

Suggested Citation

  • Michael J. Crowther, 2019. "Multilevel mixed-effects parametric survival analysis: Estimation, simulation, and application," Stata Journal, StataCorp LP, vol. 19(4), pages 931-949, December.
    • Handle: RePEc:tsj:stataj:v:19:y:2019:i:4:p:931-949
    DOI: 10.1177/1536867X19893639
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    Cited by:

    1. Adeniyi Francis Fagbamigbe & Emma Norrman & Christina Bergh & Ulla-Britt Wennerholm & Max Petzold, 2021. "Comparison of the performances of survival analysis regression models for analysis of conception modes and risk of type-1 diabetes among 1985–2015 Swedish birth cohort," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-23, June.

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