Machine learning analysis plans for randomised controlled trials: detecting treatment effect heterogeneity with strict control of type I error.
Journal:
Trials
Published Date:
Feb 10, 2020
Abstract
BACKGROUND: Retrospective exploratory analyses of randomised controlled trials (RCTs) seeking to identify treatment effect heterogeneity (TEH) are prone to bias and false positives. Yet the desire to learn all we can from exhaustive data measurements on trial participants motivates the inclusion of such analyses within RCTs. Moreover, widespread advances in machine learning (ML) methods hold potential to utilise such data to identify subjects exhibiting heterogeneous treatment response.