Constructing a Confidence Interval for the Fraction Who Benefit from Treatment Using Randomized Trial Data

• Activity Number: 28 - Personalized Medicine
• Type: Contributed
• Date/Time: Sunday, July 30, 2017 : 2:00 PM to 3:50 PM
• Sponsor: Biometrics Section
• Author(s): Emily Huang* and Ethan Fang and Daniel Hanley and Michael Rosenblum
• Companies: Johns Hopkins University Department of Biostatistics and Pennsylvania State University and Johns Hopkins Division of Brain Injury Outcomes and Johns Hopkins University
• Keywords: Treatment effect heterogeneity ; Potential outcome ; Randomized trial

Abstract

In randomized trial analyses, the average treatment effect is often used to compare treatment with control. For patients, another parameter of interest is the fraction who benefit from treatment, which is defined as the proportion of patients whose potential outcome under treatment is better than that under control. The fraction generally is only partially identified, which makes inference a challenging problem. We propose a method for constructing a confidence interval for the fraction, using randomized trial data. We consider the case of an ordinal outcome, with binary outcomes as a special case. Our method does not require any assumptions about the joint distribution of the potential outcomes. However, it can incorporate user-defined restrictions on the support of the joint distribution, such as the no harm assumption. We prove pointwise consistency of the proposed confidence interval. Through simulation, we compare it to the m-out-of-n bootstrap, with respect to coverage probability, width, and computational efficiency.