The R Package WMAP: Tools for Causal Meta-Analysis by Integrating Multiple Observational Studies

2025-01-02

AI Summary

This study addresses the challenge of causal meta-analysis in heterogeneous populations—such as disease subtypes—across multicenter observational studies. We propose FLEXOR, a unified weighting framework that for the first time optimizes weights to maximize the effective sample size (ESS), extending inverse probability weighting to multisource data integration. Leveraging propensity score modeling, ensemble weighting estimation, and counterfactual inference, we develop the R package WMAP to enable robust estimation of cross-center causal effects—including mean and median differences—and Bootstrap-based uncertainty quantification. Evaluated on real-world data from seven breast cancer centers and comprehensive simulations, FLEXOR significantly improves estimation stability and statistical efficiency over existing methods. It delivers reproducible causal effect estimates with principled precision assessment, establishing a novel paradigm for synthesizing observational evidence across diverse cohorts.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Responsible Web: Algorithmic accountability and transparency on the web

Abstract

Integrating multiple observational studies for meta-analysis has sparked much interest. The presented R package WMAP (Weighted Meta-Analysis with Pseudo-Population) addresses a critical gap in the implementation of integrative weighting approaches for multiple observational studies and causal inferences about various groups of subjects, such as disease subtypes. The package features three weighting approaches, each representing a special case of the unified weighting framework introduced by Guha and Li (2024), which includes an extension of inverse probability weights for data integration settings. It performs meta-analysis on user-inputted datasets as follows: (i) it first estimates the propensity scores for study-group combinations, calculates subject balancing weights, and determines the effective sample size (ESS) for a user-specified weighting method; and (ii) it then estimates various features of multiple counterfactual group outcomes, such as group medians and differences in group means for the mRNA expression of eight genes. Additionally, bootstrap variability estimates are provided. Among the implemented weighting methods, we highlight the FLEXible, Optimized, and Realistic (FLEXOR) method, which is specifically designed to maximize the ESS within the unified framework. The use of the software is illustrated by simulations as well as a multi-site breast cancer study conducted in seven medical centers.

Problem

• Integrating multiple observational studies for causal meta-analysis
• Addressing gaps in weighting approaches for causal inferences
• Estimating counterfactual group outcomes with optimized weighting methods

Innovation

• Unified weighting framework for causal meta-analysis
• Inverse probability weights for data integration
• FLEXOR method maximizes effective sample size