# On the Use of Weighting for Personalized and Transparent Evidence Synthesis

## TL;DR

This paper introduces a weighting framework for evidence synthesis that improves personalization and robustness in meta-analyses by integrating diverse data sources and identifying studies that deviate from target populations.

## Contribution

It extends traditional meta-analysis with a novel weighting approach that tailors results to specific patient populations and detects study deviations.

## Key findings

- Effective in simulation studies
- Improves personalization of meta-analyses
- Identifies studies deviating from target populations

## Abstract

Over the past few decades, statistical methods for causal inference have made impressive strides, enabling progress across a range of scientific fields. However, much of this methodological development has been confined to individual studies, limiting its ability to draw more generalizable conclusions. Achieving a thorough understanding of cause and effect typically relies on the integration, reconciliation, and synthesis from diverse study designs and multiple data sources. Furthermore, it is crucial to direct this synthesis effort toward understanding the effect of treatments for specific patient populations. To address these challenges, we present a weighting framework for evidence synthesis that handles both individual- and aggregate-level data, encompassing and extending conventional regression-based meta-analysis methods. We use this approach to tailor meta-analyses, targeting the covariate profiles of patients in a target population in a sample-bounded manner, thereby enhancing their personalization and robustness. We propose a technique to detect studies that meaningfully deviate from the target population, suggesting when it might be prudent to exclude them from the analysis. We establish multiple consistency conditions and demonstrate asymptotic normality for the proposed estimator. We demonstrate the effectiveness of the method through a simulation study and two real-world case studies.