Intention-to-Treat Analysis with Treatment Discontinuation and Missing Data in Clinical Trials

• Activity Number: 506
• Type: Invited
• Date/Time: Wednesday, August 12, 2015 : 10:30 AM to 12:20 PM
• Sponsor: Biometrics Section
• Author(s): Roderick Little* and Shan Kang
• Companies: University of Michigan and University of Michigan
• Keywords: clinical trials ; dropouts ; incomplete data ; intention-to-treat analysis ; missing data ; treatment discontinuation

Abstract

We discuss three aspects of the analysis of clinical trials when participants prematurely discontinue treatments. First, we distinguish treatment discontinuation from missing outcome data. Missing outcome data is a standard missing data problem, but treatment discontinuation is better viewed as a form of noncompliance, and treated using ideas from the causal literature on noncompliance. Second, the standard intention to treat (ITT) estimand, the average effect of randomization to treatment, is compared with alternative estimands for the ITT population. We argue that one of these alternatives, an on-treatment summary measure of the effect of treatment, has advantages and should receive more consideration. Third, we consider when follow-up measures after discontinuation are needed for valid measures of treatment effects. Ideas are motivated and illustrated by a reanalysis of a past study of inhaled insulin treatments for diabetes, sponsored by Eli Lilly.