Meta‐analysis of continuous outcomes: Using pseudo IPD created from aggregate data to adjust for baseline imbalance and assess treatment‐by‐baseline modification
Research Synthesis Methods, vol. 11, pp. 780–794
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.
Authors 5
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Leiden University Medical Center · Nutricia Research (Netherlands) · Danone Nutricia Research (Netherlands)
Affiliation as printed
Clinical Epidemiology Leiden University Medical Center Leiden The Netherlands
Data Science and Biometrics Danone Nutricia Research Utrecht The Netherlands
Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
Data Science and Biometrics, Danone Nutricia Research, Utrecht, The Netherlands
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Leiden University Medical Center
Affiliation as printed
Biomedical Data Sciences Leiden University Medical Center Leiden The Netherlands
Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
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Affiliation as printed
Centre for Prognosis Research, Research Institute for Primary Care & Health Sciences Keele University Keele UK
Centre for Prognosis Research, Research Institute for Primary Care & Health Sciences Keele University Keele UK
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Leiden University Medical Center
Affiliation as printed
Clinical Epidemiology Leiden University Medical Center Leiden The Netherlands
Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
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Leiden University Medical Center
Affiliation as printed
Biomedical Data Sciences Leiden University Medical Center Leiden The Netherlands
Clinical Epidemiology Leiden University Medical Center Leiden The Netherlands
Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
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