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Patient Dropout in Alzheimer’s Disease Clinical Trials | Medidata and AbbVie at AAIC 2021

Aug 06, 2021 - < 1 min read
Patient Dropout in Alzheimer’s Disease Clinical Trials | Medidata and AbbVie at AAIC 2021

Medidata and AbbVie Poster on Patient Dropout in Alzheimer’s Disease Clinical Trials at AAIC 2021

Patient dropout in Alzheimer’s Disease clinical trials is a very common challenge and can lead to trial delays, increased costs, and potentially biased trial results. In other studies, certain factors have been identified as influencing the risk of patient dropout, such as age, race, education, site facility type, and caregiver relationship. A joint team from Medidata Acorn AI and AbbVie wanted to further investigate these risk factors to see if dropout rate could be better predicted. Their findings on predictive factors of patient dropout in Alzheimer’s clinical trials were presented at the Alzheimer’s Association International Conference (AAIC) on July 26-30, 2021 in Denver, CO.

The teams used pooled clinical trial data to evaluate both additional risk factors for predicting dropout and predicting dropout risk ahead of time. They looked at pooled, cross-industry Phase 3, interventional Alzheimer’s Disease trials from the Medidata Enterprise Data Store (MEDS). Using predictive modeling, they improved dropout risk prediction by 10% over random guessing within 3, 6, and 12 months of randomization and identified new predictive factors such as the size of the clinical staff, site location characteristics, and presence and/or history of patient anxiety. These findings have implications for teams designing Alzheimer’s Disease trials.

Thank you to the authors: Kimberly Crimin, PhD1, Patricia J Allen, MS2, Iman Abba2, Corinne Ahlberg, MS2, Luke Benz2, Hiuyan Lau, PhD2, Jingshu Liu2, Fareed Melhem2, Nahome Fisseha, PharmD1 and Hana Florian, MD1

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