Application of machine learning methods in clinical trials for precision medicine.

ABSTRACT:

Objective

A key component for precision medicine is a good prediction algorithm for patients' response to treatments. We aim to implement machine learning (ML) algorithms into the response-adaptive randomization (RAR) design and improve the treatment outcomes.

Materials and methods

We incorporated 9 ML algorithms to model the relationship of patient responses and biomarkers in clinical trial design. Such a model predicted the response rate of each treatment for each new patient and provide guidance for treatment assignment. Realizing that no single method may fit all trials well, we also built an ensemble of these 9 methods. We evaluated their performance through quantifying the benefits for trial participants, such as the overall response rate and the percentage of patients

SUBMITTER: Wang Y 

PROVIDER: S-EPMC8846336 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature