Machine learning analysis plans for randomised controlled trials: detecting treatment effect heterogeneity with strict control of type I error.

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.

Methods

We present a novel analysis strategy for detecting TEH in randomised data using ML methods, whilst ensuring proper control of the false positive discovery rate. Our approach uses random data partitioning with statistical or ML-based prediction on held-out data. This method can test for

SUBMITTER: Watson JA 

PROVIDER: S-EPMC7011561 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature