Advancing Evidence Generation in Biomedical Research Using Natural Hermite and Propensity Score Indices: Applications to External Control Arms

TL;DR

This paper introduces Natural Hermite and propensity score indices to improve the reliability of external control arms in biomedical research, especially when RCTs are infeasible, by addressing data heterogeneity issues.

Contribution

It proposes novel indices for robust comparison between RCTs and real-world data, with demonstrated implementation on simulated and synthetic clinical data.

Findings

Indices improve comparison robustness

Algorithms perform well on simulated data

Enhanced external control arm reliability

Abstract

When it is not feasible to conduct randomized controlled trials (RCTs), the use of external control arms based on real-world data (RWD) may be a viable option. However, challenges arising from data heterogeneity must be addressed to ensure the reliability of trial results. We consider the use of Natural Hermite and propensity score indices to facilitate robust comparisons between RCTs and RWD studies. Illustrations are provided on the implementation and performance of the underlying algorithms using simulated data, as well as synthetic data from a clinical trial and RWD.