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Activity Number: 238 - SPEED: Biopharmaceutical Applications: Trials, Biomarkers, and Enpoint Validation
Type: Contributed
Date/Time: Monday, July 30, 2018 : 2:00 PM to 3:50 PM
Sponsor: Biopharmaceutical Section
Abstract #328938 Presentation
Title: A Bayesian Analysis of Small N Sequential Multiple Assignment Randomized Trials (SnSMARTs)
Author(s): Boxian Wei* and Kelley M Kidwell and Thomas M Braun and Roy N Tamura
Companies: University of Michigan, Ann Arbor and University of Michigan and University of Michigan and University of South Florida
Keywords: Clinical Trial; Rare Disease; Joint Model; Bias; Mean-Square Error

Designing clinical trials to study treatments for rare diseases is challenging because of the limited number of available patients. A suggested design is known as the small-n Sequential Multiple Assignment Randomized Trial (snSMART), in which patients are first randomized to one of multiple treatments (stage 1). Patients who respond to their initial treatment continue the same treatment for another stage, while those who fail to respond are re-randomized to one of the remaining treatments (stage 2). Analysis approaches for snSMARTs are limited, and we propose a Bayesian approach that allows for borrowing of information across both stages to compare the efficacy between treatments. Through simulation, we compare the bias, root mean-square error (rMSE), width and coverage rate of 96% confidence/credible interval (CI) of estimators from our approach to estimators produced from (a) standard approaches that only use the data from stage 1, and (b) a log-Poisson model using data from both stages whose parameters are estimated via generalized estimating equations. We demonstrate the rMSE and width of 95% CIs of our estimators are smaller than the other approaches in realistic settings.

Authors who are presenting talks have a * after their name.

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