Limitations in Using Multiple Imputation to Harmonize Individual Participant Data for Meta-Analysis

Juned Siddique*, Peter J. de Chavez, George Howe, Gracelyn Cruden, C. Hendricks Brown

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


Individual participant data (IPD) meta-analysis is a meta-analysis in which the individual-level data for each study are obtained and used for synthesis. A common challenge in IPD meta-analysis is when variables of interest are measured differently in different studies. The term harmonization has been coined to describe the procedure of placing variables on the same scale in order to permit pooling of data from a large number of studies. Using data from an IPD meta-analysis of 19 adolescent depression trials, we describe a multiple imputation approach for harmonizing 10 depression measures across the 19 trials by treating those depression measures that were not used in a study as missing data. We then apply diagnostics to address the fit of our imputation model. Even after reducing the scale of our application, we were still unable to produce accurate imputations of the missing values. We describe those features of the data that made it difficult to harmonize the depression measures and provide some guidelines for using multiple imputation for harmonization in IPD meta-analysis.

Original languageEnglish (US)
Pages (from-to)95-108
Number of pages14
JournalPrevention Science
StatePublished - Feb 1 2018


  • Data synthesis
  • Individual participant data meta-analysis
  • Multiple imputation
  • Posterior predictive checking

ASJC Scopus subject areas

  • Public Health, Environmental and Occupational Health

Fingerprint Dive into the research topics of 'Limitations in Using Multiple Imputation to Harmonize Individual Participant Data for Meta-Analysis'. Together they form a unique fingerprint.

Cite this