Optimising precision and power by machine learning in randomised trials with ordinal and time-to-event outcomes with an application to COVID-19.

ABSTRACT: The rapid finding of effective therapeutics requires efficient use of available resources in clinical trials. Covariate adjustment can yield statistical estimates with improved precision, resulting in a reduction in the number of participants required to draw futility or efficacy conclusions. We focus on time-to-event and ordinal outcomes. When more than a few baseline covariates are available, a key question for covariate adjustment in randomised studies is how to fit a model relating the outcome and the baseline covariates to maximise precision. We present a novel theoretical result establishing conditions for asymptotic normality of a variety of covariate-adjusted estimators that rely on machine learning (e.g., ℓ1 -regularisation, Random Forests, XGBoost, and Multivariate Adaptive Regre

SUBMITTER: Williams N 

PROVIDER: S-EPMC9539267 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature