A new Rhode Island company called Analytical Edge has announced a partnership with Brown University to develop and market a statistical methodology designed to reduce the length and cost of clinical trials by cutting the average number of participants required and allowing more flexible data analysis.

The technique, called Pure Likelihood, is a new method of measuring the strength of statistical evidence in data that "presents significant advantages for both standard and adaptive clinical trial designs," the company said. The statistical package makes it possible to better design clinical trials and analyze the results as they become available. The pharma indutsry spends more than $10 billion a year on clinical trials, and cutting costs through better statistical analysis could dent this ever-rising figure.

Using this statistical technique could allow sponsors to design clinical trials with fewer participants on average, which would in turn lower costs and speed up the trial, Analytical Edge CEO Arthur Blume told FDAnews.

The technique also offers flexibility, allowing organizers to review the data as often as they want, "with no statistical penalty for looking," Blume said. This in turn could make it possible to change trials already in progress and follow fruitful lines of inquiry while abandoning those that are not working out. Today's clinical trials contain multiple endpoints due to broad searches for toxicity and other factors, he added, and a flexible statistical package can help accommodate these multiple endpoints.