52: Reducing Unobserved Confounding and Identifying Drug Targets in Real-world Data Analysis

Thomas M. Menino Convention & Exhibition Center 

Real-world data (RWD) analysis often suffers from unobserved confounding. We integrated RWD with drug-related gene expression profiles to identify drug targets for Alzheimer's disease (AD) without generating new genetic data, while aiming to mitigate these confounding effects. We developed a meta-difference-in-differences (meta-DiD) framework to compare pooled effect sizes across cohort collections. We derived the mathematical properties of unobserved confounding in Cox models and meta-DiD frameworks, applying this to identify neuroinflammation-specific AD drug targets. A Shiny app was developed to calculate confounding effects in Cox models. Simulations demonstrated that: (i) model-based and empirical confounding effects are consistent; (ii) meta-DiD improves confounding control as cohort numbers increase or when confounding is large and homogeneous; and (iii) identified drug targets were consistently associated with AD across both RWD and differential gene expression analyses. Meta-DiD effectively controls for large, homogeneous confounding across cohorts. Integrating RWD with gene expression profiles provides a robust pipeline for drug target discovery.

Real World Data

Causal Inference

Meta-Difference-in-Difference

Unobserved Confounding

Alzheimer's Disease

Drug Target Identification 

Section on Statistics in Epidemiology