Researchers propose new method of determining clinical trial effectiveness

CAMBRIDGE, Mass. — Researchers at the Massachusetts Institute of Technology and Mayo Clinic have proposed a new way to determine if clinical trials are effective. The research was published in the Journal of American Medicine.

"Randomized clinical trials — where patients are assigned randomly to two groups, one receiving a new treatment and the other receiving a placebo — are the gold standard for determining the safety and effectiveness of a treatment," said Andrew Lo, Ph.D., the study's senior author. Only when the treatment group shows significant improvement over the placebo group, will regulators approve the therapy. Dr. Lo added the current process is designed to protect the public by preventing ineffective and unsafe therapies — "false positives" — from entering the marketplace.

Researchers studied the incorporation of patient preferences directly into the drug approval process. Dr. Lo observed that the new approach would not only be more responsive to the most urgent unmet medical needs — terminal illnesses that are currently untreatable — it could also greatly accelerate the drug development process.

At the core of this new framework — which was jointly developed by MIT researchers Lo, Shomesh Chaudhuri, M.S., and Vahid Montazerhodjat, Ph.D. (now at Boston College) and the late Mayo Clinic biostatistician Daniel J. Sargent, Ph.D — was how to quantify "significant improvement" in a clinical trial.

Dr. Lo noted the traditional approach is to set the bar high enough so that the risk of a false positive is very small, typically 2.5 percent. However, this value is fairly arbitrary, and patients with fatal diseases like pancreatic cancer or glioblastoma may be willing to take a greater risk of a false positive because their alternative is death.

The research team's method for computing the optimal risk of false positives on a case-by case basis accounts for the severity of the disease, the number of patients affected, and the value of an effective treatment to patients.

"The FDA already takes into account the urgency of unmet medical needs through a number of programs and processes; our proposed framework will allow them to incorporate the patient perspective directly into their decisions in an objective, systematic, transparent, and repeatable manner," Dr. Lo said. "Terminal patients simply can't afford to miss effective drugs that can extend their lives."

This framework is flexible enough to include a variety of stakeholder perspectives, and the authors have provided open-source software to allow anyone to re-run their analysis under different sets of assumptions for risk preferences, disease burden and prevalence, and value delivered to patients.

By allowing higher rates of false positives, more drugs will get approved for the most critical diseases, but the impact of side effects may increase as well, the researchers concluded. To address this concern, the authors recommend creating a new category of "conditional approval" which lasts only for a few years. During this time, regulators, pharma companies, and doctors will monitor patients carefully and collect more data. Depending on how the patients respond, the conditional approval can either expire or be changed to full approval.

This publication is based on data drawn from 10 existing cancer clinical trials.