Conventional medical statistics methods only quantify the average cohort-wide benefit of a drug. Hence, many drugs are licensed even if they benefit only a subset of the patients, and clinical trials fail if a detrimental effect in one patient subgroup cancels the beneficial effect in another. This avoidable situation causes unnecessary patient harm, reduced availability of drugs, and waste of resources.

 

We recently developed and implemented a new Bayesian statistical method that focuses on identifying responder subgroups in clinical trial data. In doing so, it can rescue and prevent failed clinical trials, and increase response rates of drugs by better targeting. We focus on cancer trials, where the problems are specifically acute. The benefits of responder subgroup identification, however, apply similarly to other diseases. Our method has already been tested successfully on two failed phase III cancer trials.