title: Meta-Analysis With a Continuous Covariate That Is Differentially Categorized Across Studies.
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.
authors: Jamie
authors: J
authors: Perin
authors: Christa L
authors: CL
authors: Fischer Walker
authors: Robert E
authors: RE
authors: Black
authors: Martin J
authors: MJ
authors: Aryee
mesh_terms: D000465:Algorithms; D015982:Bias; D002675:Child, Preschool; D000068598:Data Accuracy; D003967:Diarrhea / Q000453:epidemiology; D006801:Humans; D015994:Incidence; D007223:Infant; D007231:Infant, Newborn; D015201:Meta-Analysis as Topic; D015233:Models, Statistical
keywords: age; diarrhea; expectation-maximization algorithm; incidence; incomplete data; meta-regression