Popular analysis technique hides patient variability in Alzheimer's drug trials

A statistical approach being used to support a new class of Alzheimer's drugs may lead to overstated claims about how the drugs work, according to a new study led by researchers at the Brown University School of Public Health.

Published in JAMA Neurology, the research letter focused on quantile aggregation, a new statistical technique that divides people into groups, averages their results together and then looks for patterns across those groupings.

The letter examined how the approach works when applied to cognition and amyloid, a protein that builds up in the brains of people with Alzheimer's disease. The approach was originally published in an analysis of Eli Lilly and Company's Alzheimer's drug donanemab.

"Many researchers believe reducing amyloid buildup could slow memory loss and cognitive decline associated with the disease, making it a major target for newer Alzheimer's drugs," said the study's senior author Sarah Ackley, who is an assistant professor of epidemiology at Brown's School of Public Health and runs the Computational Epidemiology Lab.