Merging Multiple Longitudinal Studies with Study-Specific Missing Covariates

• Activity Number: 523
• Type: Topic Contributed
• Date/Time: Wednesday, August 12, 2015 : 10:30 AM to 12:20 PM
• Sponsor: Mental Health Statistics Section
• Author(s): Lu Wang* and Peter X.K. Song and Fei Wang
• Companies: University of Michigan and University of Michigan and Ford Motor Credit
• Keywords: Data merging ; Imputation ; Meta analysis ; Missing data ; Quadratic inference function ; Sieve estimation

Abstract

Merging multiple datasets collected with identical or similar scientific objectives is often undertaken in practice to increase statistical power. This paper 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). 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, and provide an illustrative data analysis using longitudinal cohorts collected in Mexico City to assess the effect of lead exposures on children's somatic growth.