Does including machine learning predictions in ALS clinical trial analysis improve statistical power?

ABSTRACT:

Objective

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease which leads to progressive muscle weakness and eventually death. The increasing availability of large ALS clinical trial datasets have generated much interest in developing predictive models for disease progression. However, the utility of predictive modeling on clinical trial analysis has not been thoroughly evaluated.

Methods

We evaluated a predictive modeling approach for ALS disease progression measured by ALSFRS-R using the PRO-ACT database and validated our findings in a novel test set from a former clinical trial. We examined clinical trial scenarios where model predictions could improve statistical power for detecting treatment effects with simulated clinical trials.

Results

Models co

SUBMITTER: Zhou N 

PROVIDER: S-EPMC7545604 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature