Multisite Causal Mediation Analysis in the Presence of Complex Sample and Survey Designs and Non-Random Non-Response. (14th April 2019)

• Record Type: Journal Article
• Authors: Qin, Xu; Hong, Guanglei; Deutsch, Jonah; Bein, Edward
• Is Part Of: Journal of the Royal Statistical Society. Volume 182:Number 4(2019)
• Page Start: 1343
• Page End: 1370
• Publication Date: 2019-04-14
• Subjects: Direct effect -- Indirect effect -- Multisite randomized trial -- Propensity score -- Ratio of mediator probability weighting -- Sensitivity analysis -- Treatment-by-mediator interaction; Social sciences -- Statistical methods -- Periodicals; Statistics -- Periodicals; 300.15195
• DOI: 10.1111/rssa.12446
• Languages: English
• ISSNs: 0964-1998

Abstract

Summary: This study provides a template for multisite causal mediation analysis using a comprehensive weighting-based analytic procedure that enhances external and internal validity. The template incorporates a sample weight to adjust for complex sample and survey designs, adopts an inverse probability of treatment weight to adjust for differential treatment assignment probabilities, employs an estimated non-response weight to account for non-random non-response and utilizes a propensity-score-based weighting strategy to decompose flexibly not only the population average but also the between-site heterogeneity of the total programme impact. Because the identification assumptions are not always warranted, a weighting-based balance checking procedure assesses the remaining overt bias, whereas a weighting-based sensitivity analysis further evaluates the potential bias related to omitted confounding or to propensity score model misspecification. We derive the asymptotic variance of the estimators for the causal effects that account for the sampling uncertainty in the estimated weights. The method is applied to a reanalysis of the data from the National Job Corps Study.