Bayesian probability

Internal medicine

Pathology

Mathematical economics

Random effects model

Mathematics

Type I and type II errors

Aggregate (composite)

Missing data

Meta-analysis

Statistics

Mean squared error

Covariate

Outcome (game theory)

Econometrics

Composite material

Materials science

Aggregate data

Computer science

Clinical trial

Medicine

Research Synthesis Methods

When some trials provide individual patient data (IPD) and the others provide only aggregate data (AD), meta‐analysis methods for combining IPD and AD are required. We propose a method that reconstructs the missing IPD for AD trials by a Bayesian sampling procedure and then applies an IPD meta‐analysis model to the mixture of simulated IPD and collected IPD. The method is applicable when a treatment effect can be assumed fixed across trials. We focus on situations of a single continuous outcome and covariate and aim to estimate treatment–covariate interactions separated into within‐trial and across‐trial effect. An illustration with hypertension data which has similar mean covariates across trials indicates that the method substantially reduces mean square error of the pooled within‐trial interaction estimate in comparison with existing approaches. A simulation study supposing there exists one IPD trial and nine AD trials suggests that the method has suitable type I error rate and approximately zero bias as long as the available IPD contains at least 10% of total patients, where the average gain in mean square error is up to about 40%. However, the method is currently restricted by the fixed effect assumption, and extension to random effects to allow heterogeneity is required. Copyright © 2014 John Wiley &amp; Sons, Ltd.

Meta‐analysis of a continuous outcome combining individual patient data and aggregate data: a method based on simulated individual patient data