Unconfounded Meta-analytical Frameworks for Multivariate Outcomes in Multigroup Observational Studies using Concordant Weights

TL;DR

This paper introduces a unified meta-analytical framework that uses concordant weights to analyze diverse multivariate outcomes in observational studies, addressing challenges in unplanned estimand analysis and undersampled groups.

Contribution

It develops new estimators compatible with various endpoints and weighting methods, and investigates their asymptotic properties and small-sample uncertainty quantification.

Findings

Applied to TCGA breast cancer data revealing differential gene expression patterns.

Proposed estimators effectively handle diverse outcome types and unplanned estimands.

Small-sample procedures improve uncertainty quantification for undersampled groups.

Abstract

While meta-analyzing retrospective cancer patient cohorts, an investigation of differences in the expressions of target oncogenes across cancer subtypes is of substantial interest because the results may uncover novel tumorigenesis mechanisms and improve screening and treatment strategies. Weighting methods facilitate unconfounded comparisons of multigroup potential outcomes in multiple observational studies. For example, Guha et al. (2022) introduced concordant weights, allowing integrative analyses of survival outcomes by maximizing the effective sample size. However, it remains unclear how to use this or other weighting approaches to analyze a variety of continuous, categorical, ordinal, or multivariate outcomes, especially when research interests prioritize uncommon or unplanned estimands suggested by post hoc analyses; examples include percentiles and moments of group potential outcomes and pairwise correlations of multivariate outcomes. This paper proposes a unified meta-analytical approach accommodating various types of endpoints and fosters new estimators compatible with most weighting frameworks. Asymptotic properties of the estimators are investigated under mild assumptions. For undersampled groups, we devise small-sample procedures for quantifying estimation uncertainty. We meta-analyze multi-site TCGA breast cancer data, shedding light on the differential mRNA expression patterns of eight targeted genes for the subtypes infiltrating ductal carcinoma and infiltrating lobular carcinoma.