A0227
Title: Meta-analysis by integrating multiple observational studies with multivariate outcomes
Authors: Yi Li - University of Michigan (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 is proposed, appropriate for integrative analyses. 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, and accurate small-sample procedures are devised for quantifying estimation uncertainty. Through simulation studies and meta-analyses of TCGA datasets, the differential biomarker patterns of the two major breast cancer subtypes in the United States are discovered, and the versatility and reliability of the proposed weighting strategy are demonstrated, especially for the FLEXOR pseudo-population.
