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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Veröffentlicht in:American journal of epidemiology Jg. 183; H. 5; S. 507
Hauptverfasser: Perin, Jamie, Fischer Walker, Christa L, Black, Robert E, Aryee, Martin J
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States 01.03.2016
Schlagworte:
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
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1476-6256
1476-6256
DOI:10.1093/aje/kwv140