Iterative Generalized Least Squares for Meta-Analysis of Survival Data at Multiple Times

Keith B. G. Dear

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Meta-analysis and systematic reviews Statistical Methods in Clinical Trials Statistical Methods and Bayesian Inference

Abstract

A method is presented for joint analysis of survival proportions reported at multiple times in published studies to be combined in a meta-analysis. Generalized least squares is used to fit linear models including between-trial and within-trial covariates, using current fitted values iteratively to derive correlations between times within studies. Multi-arm studies and nonrandomized historical controls can be included with no special handling. The method is illustrated with data from two previously published meta-analyses. In one, an early treatment difference is detected that was not apparent in the original analysis.

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References

1986Meta-analysis in clinical trials 1991A general parametric approach to the meta‐analysis of randomized clinical trials 1986Meta-analytic procedures for combining studies with multiple effect sizes.1988Modeling multivariate effect sizes.1992Meta‐analysis for 2 × 2 tables: A bayesian approach 1991The Future of Meta-Analysis.

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2012Meta-analysis of effect sizes reported at multiple time points: A multivariate approach 2000An Assessment of Methods to Combine Published Survival Curves 2007Impact of approximating or ignoring within‐study covariances in multivariate meta‐analyses 2008A method for the meta‐analysis of mutually exclusive binary outcomes 2009Meta‐analysis of pairs of survival curves under heterogeneity: A Poisson correlated gamma‐frailty approach 2005Synthesizing medical evidence: systematic reviews and metaanalyses 2012Chapter 12: Systematic Review of Prognostic Tests 2019A Comparison of Fixed-Effects and Random-Effects Models for Multivariate Meta-Analysis Using an SEM Approach

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