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Artif. Intell."],"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 clinical trial. We demonstrate the concept of such a computational screening tool by retrospectively analyzing a completed double-blind clinical trial of donepezil in people with amnestic mild cognitive impairment (clinicaltrials.gov: NCT00000173), identifying a data-driven subgroup having more severe cognitive impairment who showed clearer treatment response than observed for the full cohort.<\/jats:p>","DOI":"10.3389\/frai.2022.660581","type":"journal-article","created":{"date-parts":[[2022,6,3]],"date-time":"2022-06-03T10:10:03Z","timestamp":1654251003000},"update-policy":"http:\/\/dx.doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Targeted Screening for Alzheimer's Disease Clinical Trials Using Data-Driven Disease Progression Models"],"prefix":"10.3389","volume":"5","author":[{"given":"Neil P.","family":"Oxtoby","sequence":"first","affiliation":[]},{"given":"Cameron","family":"Shand","sequence":"additional","affiliation":[]},{"given":"David M.","family":"Cash","sequence":"additional","affiliation":[]},{"given":"Daniel C.","family":"Alexander","sequence":"additional","affiliation":[]},{"given":"Frederik","family":"Barkhof","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2022,5,26]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1016\/j.dadm.2019.01.005","article-title":"Predicting time to dementia using a quantitative template of disease progression","volume":"11","author":"Bilgel","year":"2019","journal-title":"Alzheimers Dement"},{"key":"B2","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1111\/cts.12491","article-title":"Lessons learned from Alzheimer disease: clinical trials with negative outcomes","volume":"11","author":"Cummings","year":"2018","journal-title":"Clin. 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