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Activity Number: 243 - Contributed Poster Presentations: Biopharmaceutical Section
Type: Contributed
Date/Time: Monday, July 31, 2017 : 2:00 PM to 3:50 PM
Sponsor: Biopharmaceutical Section
Abstract #323155
Title: Two-Stage Design for Phase II Cancer Clinical Trials with Multiple Endpoints
Author(s): Hui Gu* and Yong Lin and Weichung Joe Shih and Yaqun Wang and Kejian Liu
Companies: Rutgers University and Rutgers and Rutgers School of Public Health and Rutgers and Celgene
Keywords: Two-stage optimal design ; Phase II cancer trial ; optimization ; alternative primary endpoints
Abstract:

The main purpose of a single-arm phase II cancer trial of a new regimen is to determine whether it has sufficient anti-tumor activity against a specific type of tumor to warrant its further clinical development. Such a research question can be answered under the framework of hypothesis testing. With the advent of targeted therapies that prolong disease stabilization, cancer patients typically experience stable disease (SD) rather than tumor shrinkage. It has been shown that patients with SD also achieve clinical benefits. Therefore, when evaluating the anti-tumor activity of a new treatment, clinicians are interested not only in overall response rate (complete or partial response(s)), but also in other types of measurements indicating clinical benefit. Taking two primary efficacy endpoints as an example, if the new treatment can improve on either endpoint(s), it may be promising for further evaluation. Therefore, "OR" logical relationship between the two primary efficacy endpoints is used when specifying the alternative hypothesis. In phase II cancer clinical trials, two-stage designs rather than single-stage ones are widely used for its possibility of early termination for futility to protect cancer patients. Motivated by two real cancer clinical trials, we propose a single-arm two-stage phase II cancer clinical trial design with two dichotomous alternative primary efficacy endpoints. Because of unknown correlation between two endpoints at the design stage, minimax rule is used to determine the optimal design, which minimizes the maximum of the expected sample size among all possible correlations, subject to the type I and II error constraints. Optimal designs for a variety of design parameters as well as the corresponding operating characteristics are provided.


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

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