Machine Learning Predictive Models Can Improve Efficacy of Clinical Trials for Alzheimer's Disease
According to the article authors, the ideal participants for Alzheimer's disease clinical trials would show cognitive decline in the absence of treatment (i.e., placebo arm) and also would be responsive to the therapeutic intervention being studied (i.e., drug arm). This investigation tested whether machine learning models can effectively predict cognitive decline in people with mild to moderate Alzheimer’s disease during the timeframe of a phase III clinical trial. Data from 202 participants with a diagnosis of Alzheimer’s at baseline from the Alzheimer's Disease Neuroimaging Initiative (ADNI) was used to train machine learning classifiers to differentiate between individuals who had declining cognitive function and individuals with stable cognitive function. The authors concluded that machine learning predictive models can be effectively used to boost the power of clinical trials by reducing the sample size.
Ezzati A, Lipton RB, for the Alzheimer’s Disease Neuroimaging Initiative. Machine learning predictive models can improve efficacy of clinical trials for Alzheimer’s disease. Journal of Alzheimer's Disease 2020;74(1):55-63.