Multiple imputation for harmonizing longitudinal non-commensurate measures in individual participant data meta-analysis

Juned Siddique*, Jerome P. Reiter, Ahnalee Brincks, Robert D. Gibbons, Catherine M. Crespi, C. Hendricks Brown

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

30 Scopus citations


There are many advantages to individual participant data meta-analysis for combining data from multiple studies. These advantages include greater power to detect effects, increased sample heterogeneity, and the ability to perform more sophisticated analyses than meta-analyses that rely on published results. However, a fundamental challenge is that it is unlikely that variables of interest are measured the same way in all of the studies to be combined. We propose that this situation can be viewed as a missing data problem in which some outcomes are entirely missing within some trials and use multiple imputation to fill in missing measurements. We apply our method to five longitudinal adolescent depression trials where four studies used one depression measure and the fifth study used a different depression measure. None of the five studies contained both depression measures. We describe a multiple imputation approach for filling in missing depression measures that makes use of external calibration studies in which both depression measures were used. We discuss some practical issues in developing the imputation model including taking into account treatment group and study. We present diagnostics for checking the fit of the imputation model and investigate whether external information is appropriately incorporated into the imputed values.

Original languageEnglish (US)
Pages (from-to)3399-3414
Number of pages16
JournalStatistics in Medicine
Issue number26
StatePublished - Nov 20 2015


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

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability


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