Causal Meta-Analysis by Integrating Multiple Observational Studies with Multivariate Outcomes

TL;DR

This paper introduces a covariate-balancing meta-analysis framework with a novel FLEXOR weighting method, enabling unconfounded causal inferences from multiple observational studies with diverse outcomes.

Contribution

It extends weighting methods to integrate multiple retrospective cohorts with unbalanced covariates and develops new estimators for population-level features.

Findings

FLEXOR weighting improves covariate balance and effective sample size.

The proposed estimators are asymptotically valid and reliable.

Simulation and TCGA data analyses demonstrate method effectiveness.

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. We propose a general covariate-balancing framework based on pseudo-populations that extends established weighting methods to the meta-analysis of multiple retrospective cohorts with multiple groups. Additionally, by maximizing the effective sample sizes of the cohorts, we propose a FLEXible, Optimized, and Realistic (FLEXOR) weighting method appropriate for integrative analyses. We develop new weighted estimators for unconfounded inferences on wide-ranging population-level features and estimands relevant to group comparisons of quantitative, categorical, or multivariate outcomes. Asymptotic properties of these estimators are examined. Through simulation studies and meta-analyses of TCGA datasets, we demonstrate the versatility and reliability of the proposed weighting strategy, especially for the FLEXOR pseudo-population.