Statistics Chairman William Rosenberger wants to improve the odds of successful clinical trials

“What we are doing is trying to find treatments that are tailored to specific patients who respond best to them."

William Rosenberger, university professor and chairman of the Department of Statistics

William F. Rosenberger, university professor and chairman of Mason Engineering's Department of Statistics, works on improving clinical trial methodology.

William F. Rosenberger, university professor and chairman of Mason Engineering's Department of Statistics, works on improving clinical trial methodology to advance medical research with respect to efficiency and ethics.

William Rosenberger would like to see more favorable odds for patients in clinical trials. So the University Professor and chair of Mason Engineering’s Department of Statistics is helping medical researchers apply the concepts used in personalized medicine to better design studies—and benefit patients.

In personalized medicine, patients get targeted treatments based on their genetics, specific illness, or disease, and other characteristics.   

For years, medical researchers have conducted large-scale studies on the general population. They use statistical analyses to evaluate the safety and effectiveness of experimental treatments, such as new medications or medical devices. Many studies have failed because the therapies didn’t work on the population at large, but they may have worked in a subgroup of patients, he says.

Instead of using the older protocol, Rosenberger and his doctoral students are developing enrichment design methodology to help scientists pinpoint new experimental treatments that work for some people, as is done in personalized medicine.

Here’s one way it works:

Researchers start out by testing several new medical therapies on a large population. At an interim point of the study, they look at the initial results, do some statistical decision-making with the help of statisticians like Rosenberger, and narrow down the treatments to those that seem to be working the best.  

Adaptive designs can also identify the patients who are responding best to treatments and weight the study to favor assigning treatments that work best for the patients in the clinical trials. This is called covariate-adjusted response-adaptive (CARA) randomization, Rosenberger says.

He is particularly interested in designing clinical trials for rare diseases, which led him to serve as a member of an international advisory board for a European Union consortium on small population clinical trials. Changing the way the studies are conducted could make it easier for people to get new therapies earlier and potentially save lives.

“The methodology of CARA enrichment designs has the potential to impact the way we think about designing clinical trials in the future,” he says. “It could lead to more efficient study designs that benefit patients."

This story appears in the summer issue of Mason Spirit magazine.