Multilevel mixed-effects parametric survival analysis

Michael J. Crowther
Additional contact information
Michael J. Crowther: Centre for Biostatistics and Genetic Epidemiology, University of Leicester

United Kingdom Stata Users' Group Meetings 2013 from Stata Users Group

Abstract: Multilevel mixed-effects survival models are used in the analysis of clustered survival data, such as repeated events, multicenter clinical trials, or individual patient data meta-analyses, to investigate heterogeneity in baseline risk and treatment effects. I present the stmixed command for the parametric analysis of clustered survival data with two levels. Mixed-effects parametric survival models available include the exponential, Weibull and Gompertz proportional-hazards models, the Royston–Parmar flexible-parametric model, and the log–logistic, log–normal, and generalized gamma-accelerated failure-time models. Estimation is conducted using maximum likelihood, with both adaptive and nonadaptive Gauss–Hermite quadrature available. I will illustrate the command through simulation and application to clinical datasets.

Date: 2013-09-16
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://repec.org/usug2013/crowther.uk13.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:boc:usug13:05

Access Statistics for this paper

More papers in United Kingdom Stata Users' Group Meetings 2013 from Stata Users Group Contact information at EDIRC.
Bibliographic data for series maintained by Christopher F Baum ().