External control arm analysis: an evaluation of propensity score approaches, G-computation, and doubly debiased machine learning.

ABSTRACT:

Background

An external control arm is a cohort of control patients that are collected from data external to a single-arm trial. To provide an unbiased estimation of efficacy, the clinical profiles of patients from single and external arms should be aligned, typically using propensity score approaches. There are alternative approaches to infer efficacy based on comparisons between outcomes of single-arm patients and machine-learning predictions of control patient outcomes. These methods include G-computation and Doubly Debiased Machine Learning (DDML) and their evaluation for External Control Arms (ECA) analysis is insufficient.

Methods

We consider both numerical simulations and a trial replication procedure to evaluate the different statistical approaches: propensity score m

SUBMITTER: Loiseau N 

PROVIDER: S-EPMC9795588 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature