But in an adaptive trial, the trial's statistical algorithm constantly monitors the results from the first volunteers, and looks for any sign that one treatment is better than another. It doesn't tell the patients or the study doctors what they're seeing, but they do start randomly assigning slightly more patients into the group that's getting the treatment that is starting to look better. In other words, the trial "learns" along the way.
"It's a way of assigning patients at slightly less than random chance, allowing us to do what might be in the best interest of each patient as the trial goes along," says Meurer, an assistant professor of emergency medicine and neurology at the U-M Medical School.
By the end of the trial, one of the groups of patients will therefore be larger, which means the statistical analysis of the results will be trickier and the results might be a little less definitive. But if the number of patients in the trial is large, and if the difference between treatments is sizable, the results will still have scientific validity, Meurer says.
A clinical trial of post-stroke blood sugar treatment is one example of this kind of approach. It's being coordinated by the Neurological Emergencies Treatment Trial network based at U-M, and conducted at dozens of centers including, in coming months, U-M's own emergency department and inpatient stroke unit.
The study, called SHINE, uses an adaptive method of assigning stroke survivors to a target blood sugar level in the first day after their stroke with the goal of finding out how much impact blood sugar control has on how well the patients do overall. The study was designed by researchers at th
|Contact: Kara Gavin|
University of Michigan Health System