title: Merging multiple longitudinal studies with study-specific missing covariates: A joint estimating function approach.
abstract: Merging multiple datasets collected from studies with identical or similar scientific objectives is often undertaken in practice to increase statistical power. This article concerns the development of an effective statistical method that enables to merge multiple longitudinal datasets subject to various heterogeneous characteristics, such as different follow-up schedules and study-specific missing covariates (e.g., covariates observed in some studies but missing in other studies). The presence of study-specific missing covariates presents great statistical methodology challenge in data merging and analysis. We propose a joint estimating function approach to addressing this challenge, in which a novel nonparametric estimating function constructed via splines-based sieve approximation is utilized to bridge estimating equations from studies with missing covariates to those with fully observed covariates. Under mild regularity conditions, we show that the proposed estimator is consistent and asymptotically normal. We evaluate finite-sample performances of the proposed method through simulation studies. In comparison to the conventional multiple imputation approach, our method exhibits smaller estimation bias. We provide an illustrative data analysis using longitudinal cohorts collected in Mexico City to assess the effect of lead exposures on children's somatic growth.
authors.affiliation: Global Analytics, Ford Motor Credit, Dearborn, Michigan 48126, U.S.A.
authors.forename: Fei
authors.initials: F
authors.lastname: Wang
authors.affiliation: Department of Biostatistics, University of Michigan, Ann Arbor, Michigan 48109, U.S.A.
authors.forename: Peter X-K
authors.initials: PX
authors.lastname: Song
authors.affiliation: Department of Biostatistics, University of Michigan, Ann Arbor, Michigan 48109, U.S.A.
authors.forename: Lu
authors.initials: L
authors.lastname: Wang
mesh_terms: D001699:Biometry / Q000379:methods*; D001835:Body Weight / Q000187:drug effects; D002657:Child Development / Q000187:drug effects; D002675:Child, Preschool; D003198:Computer Simulation; D005260:Female; D005312:Fetal Blood / Q000378:metabolism; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D007854:Lead / Q000097:blood / Q000633:toxicity; D008137:Longitudinal Studies; D008297:Male; D015233:Models, Statistical; D015999:Multivariate Analysis
chemical_list: D007854:Lead
keywords: Data merging; Imputation; Meta analysis; Quadratic inference function; Sieve estimation