New Method Improves on Alzheimer's Disease Clinical Trials

Nishant Verma (Ph.D. 2015), a researcher who studied with Professor Mia Markey, coauthored a paper that describes a new method for measuring the progression of Alzheimer's Disease. His new method of measurement improves the efficiency of clinical trials for Alzheimer's patients and could be critical in finding a treatment for the disease.

Alzheimer's Disease is the most common form of age-related dementia, and affects over 44 million elderly people worldwide—a number that is expected to increase over the next few years. There are no current treatments for Alzheimer's Disease, though more than 400 treatments were studied in clinical trials over the past decade. The clinical trials were unable to demonstrate conclusive evidence of slowing disease progression, so the failure rate of Alzheimer's clinical trials is the highest of any disease.

The current rating scale, called the Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog), has been prone to poor sensitivity in tracking the progression of Alzheimer's Disease. This means that if a treatment in clinical trials did slow down the disease, the ADAS-Cog might not detect it. That's where Verma's research comes in.

In his research, Verma studied the ADAS-Cog system to determine limitations that led to low sensitivity and then improved upon them. Using a modern psychometric technique called item response theory, he developed a new scoring methodology for the ADAS-Cog. He evaluated his new scoring system with simulated clinical trials and real ones, and found significant improvements in detection.

Read an author blog describing the research.

Read the paper.