Targeted Screening for Alzheimer's Disease Clinical Trials Using Data-Driven Disease Progression Models.

ABSTRACT: Heterogeneity in Alzheimer's disease progression contributes to the ongoing failure to demonstrate efficacy of putative disease-modifying therapeutics that have been trialed over the past two decades. Any treatment effect present in a subgroup of trial participants (responders) can be diluted by non-responders who ideally should have been screened out of the trial. How to identify (screen-in) the most likely potential responders is an important question that is still without an answer. Here, we pilot a computational screening tool that leverages recent advances in data-driven disease progression modeling to improve stratification. This aims to increase the sensitivity to treatment effect by screening out non-responders, which will ultimately reduce the size, duration, and cost of a clinica

SUBMITTER: Oxtoby NP 

PROVIDER: S-EPMC9204250 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature