Meta‐analysis of continuous outcomes: Using pseudo IPD created from aggregate data to adjust for baseline imbalance and assess treatment‐by‐baseline modification

Katerina Papadimitropoulou · Theo Stijnen · Richard D Riley · Olaf M. Dekkers · Saskia le Cessie

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

Abstract

Meta-analysis of individual participant data (IPD) is considered the "gold-standard" for synthesizing clinical study evidence. However, gaining access to IPD can be a laborious task (if possible at all) and in practice only summary (aggregate) data are commonly available. In this work we focus on meta-analytic approaches of comparative studies where aggregate data are available for continuous outcomes measured at baseline (pre-treatment) and follow-up (post-treatment). We propose a method for constructing pseudo individual baselines and outcomes based on the aggregate data. These pseudo IPD can be subsequently analysed using standard analysis of covariance (ANCOVA) methods. Pseudo IPD for continuous outcomes reported at two timepoints can be generated using the sufficient statistics of an ANCOVA model, i.e., the mean and standard deviation at baseline and follow-up per group, together with the correlation of the baseline and follow-up measurements. Applying the ANCOVA approach, which crucially adjusts for baseline imbalances and accounts for the correlation between baseline and change scores, to the pseudo IPD, results in identical estimates to the ones obtained by an ANCOVA on the true IPD. In addition, an interaction term between baseline and treatment effect can be added. There are several modeling options available under this approach, which makes it very flexible. Methods are exemplified using reported data of a previously published IPD meta-analysis of 10 trials investigating the effect of antihypertensive treatments on systolic blood pressure, leading to identical results compared with the true IPD analysis and of a meta-analysis of fewer trials, where baseline imbalance occurred.

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References

1986Meta-analysis in clinical trials 2010Cochrane Handbook for Systematic Reviews of Interventions 2012Enhanced secondary analysis of survival data: reconstructing the data from published Kaplan-Meier survival curves 2010Meta-analysis of individual participant data: rationale, conduct, and reporting 2010Random effects meta-analysis of event outcome in the framework of the generalized linear mixed model with applications in sparse data 2002To IPD or not to IPD?2005Meta-analysis of individual patient data from randomized trials: a review of methods used in practice 2016Meta-analysis using individual participant data: one-stage and two-stage approaches, and why they may differ

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