Individual treatment effects: implications for research, clinical practice, and policy

Abstract

Funding for comparative effectiveness research (CER) has focused attention on what treatments work best under what specific clinical circumstances, and for whom. Because not all patients respond in the same way, treatment decisions, clinical guidelines, and coverage policies applied in a "one-size-fits-all" fashion based upon the population "average" response may lead to suboptimal outcomes. Existing frameworks focus on why patients respond differently to treatments. We propose a framework that identifies when these differences are likely to be clinically important. Scenarios are presented in which it may be most critical for clinical decisions and policies to distinguish between the average and the individual patient so that treatment recommendations provide the greatest benefits for the largest number of patients. We provide recommendations for researchers to help identify issues to study, for providers to help assist them in recommending optimal treatment for individual patients, and for payers or public health bodies to help balance societal needs with those of the individual.