Meta-Analysis With a Continuous Covariate That Is Differentially Categorized Across Studies

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-maximiza...

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Bibliographic Details
Published in:American journal of epidemiology Vol. 183; no. 5; p. 507
Main Authors: Perin, Jamie, Fischer Walker, Christa L, Black, Robert E, Aryee, Martin J
Format: Journal Article
Language:English
Published: United States 01.03.2016
Subjects:
Algorithms
Bias
Child, Preschool
Data Accuracy
Diarrhea - epidemiology
Humans
Incidence
Infant
Infant, Newborn
Meta-Analysis as Topic
Models, Statistical
expectation-maximization algorithm
diarrhea
incidence
age
incomplete data
meta-regression
ISSN:1476-6256, 1476-6256
Online Access:Get more information
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Summary: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.
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ISSN:1476-6256
1476-6256
DOI:10.1093/aje/kwv140