One-stage parametric meta-analysis of time-to-event outcomes

Methodology for the meta‐analysis of individual patient data with survival end‐points is proposed. Motivated by questions about the reliance on hazard ratios as summary measures of treatment effects, a parametric approach is considered and percentile ratios are introduced as an alternative to hazard...

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Bibliographic Details
Published in:Statistics in medicine 2010-12, Vol.29 (29), p.3030-3045
Main Authors: Siannis, F., Barrett, J. K., Farewell, V. T., Tierney, J. F.
Format: Article
Language:English
Subjects:
Algorithms
Computer Simulation
extended log-gamma model
Glioma - drug therapy
Glioma - mortality
Glioma - therapy
Humans
individual patient data
Likelihood Functions
Logistic Models
Medical statistics
Meta-analysis
Meta-Analysis as Topic
Models, Statistical
Parameter estimation
proportional hazards
Proportional Hazards Models
Randomized Controlled Trials as Topic
Regression Analysis
Simulation
Statistical Distributions
Survival analysis
Survival Rate
time-to-event outcomes
Treatment Outcome
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Summary:Methodology for the meta‐analysis of individual patient data with survival end‐points is proposed. Motivated by questions about the reliance on hazard ratios as summary measures of treatment effects, a parametric approach is considered and percentile ratios are introduced as an alternative to hazard ratios. The generalized log‐gamma model, which includes many common time‐to‐event distributions as special cases, is discussed in detail. Likelihood inference for percentile ratios is outlined. The proposed methodology is used for a meta‐analysis of glioma data that was one of the studies which motivated this work. A simulation study exploring the validity of the proposed methodology is available electronically. Copyright © 2010 John Wiley & Sons, Ltd.
ISSN:0277-6715
1097-0258
DOI:10.1002/sim.4086