Estimating individual treatment effects on COPD exacerbations by causal machine learning on randomised controlled trials.

ABSTRACT:

Rationale

Estimating the causal effect of an intervention at individual level, also called individual treatment effect (ITE), may help in identifying response prior to the intervention.

Objectives

We aimed to develop machine learning (ML) models which estimate ITE of an intervention using data from randomised controlled trials and illustrate this approach with prediction of ITE on annual chronic obstructive pulmonary disease (COPD) exacerbation rates.

Methods

We used data from 8151 patients with COPD of the Study to Understand Mortality and MorbidITy in COPD (SUMMIT) trial (NCT01313676) to address the ITE of fluticasone furoate/vilanterol (FF/VI) versus control (placebo) on exacerbation rate and developed a novel metric, Q-score, for assessing the power of causal infe

SUBMITTER: Verstraete K 

PROVIDER: S-EPMC10511983 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature