Related Experiment Video
Updated: Apr 11, 2026

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment
Published on: April 19, 2024
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.
Abstract:
Meta-analysis of time-to-event data has proved difficult in the past because consistent summary statistics often cannot be extracted from published results. The use of individual patient data allows for the re-analysis of each study in a consistent fashion and thus makes meta-analysis of time-to-event data feasible. Time-to-event data can be analysed using proportional hazards models, but incorporating random effects into these models is not straightforward in standard software. This paper fits random-effects proportional hazards models by treating the random effects as missing data and applying the expectation-maximisation algorithm. This approach has been used before by using Markov chain Monte Carlo methods to perform the expectation step of the algorithm. In this paper, the expectation step is simplified, without sacrificing accuracy, by approximating the expected values of the random effects using simple shrinkage estimators. This provides a robust method for fitting random-effects models that can be implemented in standard statistical packages. Copyright © 2012 John Wiley & Sons, Ltd.
Related Concept Videos
Comparing the Survival Analysis of Two or More Groups
Censoring Survival Data
Introduction To Survival Analysis
The primary goal of survival analysis is to estimate survival time—the time...
Kaplan-Meier Approach
Parametric Survival Analysis: Weibull and Exponential Methods
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
Assumptions of Survival Analysis

