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

ABSTRACT: 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 diffe

SUBMITTER: Siddique J 

PROVIDER: S-EPMC4596762 | biostudies-literature | 2015 Nov

REPOSITORIES: biostudies-literature