Generalizing Clinical Trials with Convex Hulls

TL;DR

This paper introduces OCH, an algorithm that extrapolates clinical trial results to broader populations by combining observational and trial data through convex hulls, addressing confounding and strict exclusion criteria.

Contribution

The paper proposes a novel method, OCH, that uses convex hulls to integrate observational and trial data for more accurate treatment effect estimation in diverse populations.

Findings

OCH achieves state-of-the-art accuracy in estimating treatment effects.

Theoretical analysis supports the linear mixing assumption.

Method effectively generalizes trial results to broader populations.

Abstract

Randomized clinical trials eliminate confounding but impose strict exclusion criteria that limit recruitment to a subset of the population. Observational datasets are more inclusive but suffer from confounding -- often providing overly optimistic estimates of treatment response over time due to partially optimized physician prescribing patterns. We therefore assume that the unconfounded treatment response lies somewhere in-between the observational estimate before and the observational estimate after treatment assignment. This assumption allows us to extrapolate results from exclusive trials to the broader population by analyzing observational and trial data simultaneously using an algorithm called Optimum in Convex Hulls (OCH). OCH represents the treatment effect either in terms of convex hulls of conditional expectations or convex hulls (also known as mixtures) of conditional densities. The algorithm first learns the component expectations or densities using the observational data and then learns the linear mixing coefficients using trial data in order to approximate the true treatment effect; theory importantly explains why this linear combination should hold. OCH estimates the treatment effect in terms both expectations and densities with state of the art accuracy.