Computational Phenomapping of Randomized Clinical Trials to Enable Assessment of their Real-world Representativeness and Personalized Inference.

ABSTRACT:

Background

Randomized clinical trials (RCTs) define evidence-based medicine, but quantifying their generalizability to real-world patients remains challenging. We propose a multidimensional approach to compare individuals in RCT and electronic health record (EHR) cohorts by quantifying their representativeness and estimating real-world effects based on individualized treatment effects (ITE) observed in RCTs.

Methods

We identified 65 pre-randomization characteristics of an RCT of heart failure with preserved ejection fraction (HFpEF), the Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist Trial (TOPCAT), and extracted those features from patients with HFpEF from the EHR within the Yale New Haven Health System. We then assessed the real-world

SUBMITTER: Thangaraj PM 

PROVIDER: S-EPMC11118629 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature