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Individual patient data meta‐analysis of time‐to‐event outcomes: one‐stage versus two‐stage approaches for estimating the hazard ratio under a random effects model
Author(s) -
Bowden Jack,
Tierney Jayne F,
Simmonds Mark,
Copas Andrew J,
Higgins Julian PT
Publication year - 2011
Publication title -
research synthesis methods
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.376
H-Index - 35
eISSN - 1759-2887
pISSN - 1759-2879
DOI - 10.1002/jrsm.45
Subject(s) - stage (stratigraphy) , random effects model , event (particle physics) , meta analysis , hazard ratio , statistics , hazard , proportional hazards model , event data , computer science , confidence interval , econometrics , medicine , mathematics , paleontology , physics , chemistry , organic chemistry , quantum mechanics , covariate , biology
Meta‐analyses of individual patient data (IPD) provide a strong and authoritative basis for evidence synthesis. IPD are particularly useful when the outcome of interest is the time to an event. Methodological developments now enable the meta‐analysis of time‐to‐event IPD using a single model, allowing treatment effect and across‐trial heterogeneity parameters to be estimated simultaneously. This differs from the standard approaches used with aggregate data, and also predominantly with IPD. Facilitated by a simulation study, we investigate what these new ‘one‐stage’ random‐effects models offer over standard ‘two‐stage’ approaches. We find that two‐stage approaches represent a robust, reliable and easily implementable way to estimate treatment effects and account for heterogeneity. Nevertheless, one‐stage models can be used to provide a deeper insight into the data. Software for fitting one‐stage Cox models with random effects using Restricted Maximum Likelihood methodology is made available, and its use demonstrated on an IPD meta‐analysis assessing post‐operative radio therapy for patients with non‐small cell lung cancer. Copyright © 2011 John Wiley & Sons, Ltd.