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

Yusuke Yamaguchi, Wataru Sakamoto, Masashi Goto, Jan A. Staessen, Jiguang Wang, Francois Gueyffier, Richard D. Riley

Research output: Contribution to journalArticle


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.

Original languageEnglish
Pages (from-to)322-351
Number of pages30
JournalResearch Synthesis Methods
Issue number4
Publication statusPublished - Dec 1 2014



  • Individual patient data
  • Meta-analysis
  • Statistical simulation
  • Treatment-covariate interaction

ASJC Scopus subject areas

  • Education

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