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Random-effects meta-analysis of time-to-event data using the expectation-maximisation algorithm and shrinkage
Mark C Simmonds1, Julian Pt Higgins1,2, Lesley A Stewart1
1Centre for Reviews and Dissemination, University of York, York, UK.
Research Synthesis Methods
|June 9, 2015
Summary
Meta-analysis of time-to-event data is now feasible using individual patient data. This study introduces a robust method for fitting random-effects models, simplifying analysis for complex survival data.
Area of Science:
- Biostatistics
- Survival Analysis
- Statistical Modeling
Background:
- Meta-analysis of time-to-event data presents challenges due to inconsistent summary statistics in published studies.
- Individual patient data enables consistent re-analysis, making meta-analysis of survival data feasible.
Purpose of the Study:
- To develop a robust and implementable method for fitting random-effects proportional hazards models for time-to-event data meta-analysis.
- To simplify the expectation-maximisation algorithm for fitting these models.
Main Methods:
- Fitting random-effects proportional hazards models by treating random effects as missing data.
- Utilizing the expectation-maximisation algorithm with a simplified expectation step.
- Approximating expected values of random effects using shrinkage estimators.
Main Results:
- The proposed method simplifies the expectation step of the expectation-maximisation algorithm without compromising accuracy.
- This approach provides a robust technique for fitting random-effects models.
- The method is implementable in standard statistical packages.
Conclusions:
- This paper presents a practical and accurate method for conducting meta-analysis of time-to-event data using random-effects models.
- The simplified expectation-maximisation approach enhances the feasibility of complex survival data meta-analysis.
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