Meta-analysis with a continuous covariate that is differentially categorized across studies

Jamie Perin, Christa L.Fischer Walker, Robert E. Black, Martin J. Aryee

Research output: Contribution to journalArticle

Abstract

We propose taking advantage of methodology for missing data to estimate relationships and adjust outcomes in a meta-analysis where a continuous covariate is differentially categorized across studies. The proposed method incorporates all available data in an implementation of the expectation-maximization algorithm. We use simulations to demonstrate that the proposed method eliminates bias that would arise by ignoring a covariate and generalizes the meta-analytical approach for incorporating covariates that are not uniformly categorized. The proposed method is illustrated in an application for estimating diarrhea incidence in children aged ≤59 months.

Original languageEnglish (US)
Pages (from-to)507-514
Number of pages8
JournalAmerican journal of epidemiology
Volume183
Issue number5
DOIs
StatePublished - Mar 1 2016

Keywords

  • age
  • diarrhea
  • expectation-maximization algorithm
  • incidence
  • incomplete data
  • meta-regression

ASJC Scopus subject areas

  • Epidemiology

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