Reverse Regression in Randomized Clinical Trials with Nonignorable Nonresponse

• Activity Number: 118
• Type: Topic Contributed
• Date/Time: Monday, July 30, 2012 : 8:30 AM to 10:20 AM
• Sponsor: Biopharmaceutical Section
• Author(s): Zhiwei Zhang*+ and Kyeongmi Cheon
• Companies: FDA/CDRH and National Institute of Child Health and Human Development
• Keywords: density ratio ; logistic regression ; missing data ; missing not at random ; pseudo-likelihood ; treatment comparison

Abstract

A common problem in randomized clinical trials is nonignorable nonresponse, which occurs frequently when the continued participation of patients depends heavily on the (perceived) level of treatment success. Standard methods for handling nonignorable nonresponse typically require specification of the response mechanism, which can be difficult in practice. This article proposes a reverse regression approach that does not require a model for the response mechanism. Instead, the proposed approach relies on the assumption that nonresponse is conditionally independent of treatment assignment given the outcome. This conditional independence assumption is motivated by the observation that, when patients are effectively masked to the assigned treatment, their decision to either stay in the trial or drop out cannot depend on the assigned treatment directly. Under this assumption, one can estimate parameters in the reverse regression model, test for a favorable treatment effect, and in some cases estimate the outcome distributions. The proposed approach is evaluated in a simulation study and illustrated with real data from a cardiovascular study.