A1159
Title: Causal meta-analysis by integrating multiple observational studies with multivariate outcomes
Authors: Yi Li - University of Michigan (United States)
Subharup Guha - University of Florida (United States) [presenting]
Abstract: Integrating multiple observational studies to make unconfounded causal or descriptive comparisons of group potential outcomes in a large natural population is challenging. Moreover, retrospective cohorts, being convenience samples, are usually unrepresentative of the natural population of interest and have groups with unbalanced covariates. A general covariate-balancing framework is proposed based on pseudo-populations that extend established weighting methods to the meta-analysis of multiple retrospective cohorts with multiple groups. Additionally, by maximizing the effective sample sizes of the cohorts, a FLEXible, Optimized, and Realistic (FLEXOR) weighting method, appropriate for integrative analyses, is proposed. New weighted estimators are developed for unconfounded inferences on wide-ranging population-level features and estimands relevant to group comparisons of quantitative, categorical, or multivariate outcomes. The asymptotic properties of these estimators are examined. Through simulation studies and meta-analyses of TCGA datasets, the versatility and reliability of the proposed weighting strategy is demonstrated, especially for the FLEXOR pseudo-population.
