Extension of the Two-Stage Randomized Trial Design for Testing Treatment, Self-Selection, and Treatment Preference Effects to Binary Outcomes

• Activity Number: 337 - Interim Monitoring and Analyses: Two-Stage, Multi-Stage, and Group Sequential Designs
• Type: Contributed
• Date/Time: Tuesday, August 1, 2017 : 10:30 AM to 12:20 PM
• Sponsor: Biopharmaceutical Section
• Author(s): Briana Cameron* and Denise Esserman and Peter Peduzzi
• Companies: Yale University and Yale School of Public Health and Yale School of Public Health
• Keywords: two-stage ; preference ; sample size ; binomial

Abstract

While traditional clinical trials seek to determine treatment efficacy within a specified population, they often ignore the role of a patient's treatment preference on his or her treatment response. The two-stage (doubly) randomized preference trial design provides one approach for researchers seeking to disentangle preference effects from treatment effects. Currently, this two-stage design is limited to the design and analysis of continuous outcome variables; in this presentation, we extend this current design to include binary variables. First, we present the test statistics for testing the preference, selection and treatment effects in a two-stage randomized design with a binary outcome measure, with and without stratification. We also derive closed-form sample size formulae to indicate the number of patients needed to detect each effect. A series of simulation studies were done to explore the properties and efficiency of both the un-stratified and stratified doubly randomized trial designs. Finally, we demonstrate the applicability of these methods using an example of a trial of Hepatitis C treatment.