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Activity Number: 138
Type: Contributed
Date/Time: Monday, August 10, 2015 : 8:30 AM to 10:20 AM
Sponsor: WNAR
Abstract #317023 View Presentation
Title: Identifying a Patient Subgroup with Differential Treatment Benefit
Author(s): David Prince* and Susanne May and Scott Emerson and Michael LeBlanc and Erika Thommes
Companies: University of Washington and University of Washington and University of Washington and Fred Hutchinson Cancer Research Center and University of Washington
Keywords: Clinical trials ; Subgroups ; Logic regression
Abstract:

Our increased understanding of genomics and related fields has led to: 1) the identification of subtypes of cancer based on various biomarkers and 2) to the development of drugs to target specific subtypes of cancer. Clinical trials test whether these novel treatments are effective in a population. Deciding which patients to enroll in a confirmatory, or phase III, trial can be difficult, but phase II trial results are informative. We propose using logic regression, a tree-based classification algorithm, on such results. The method identifies a subgroup of patients that have differential treatment benefit by finding a Boolean, or logical, statement of binary baseline covariates most strongly associated with an outcome. We have developed two adaptations of this method for binary and continuous outcomes along with two decision rules to determine who to enroll in a phase III trial. In simulation studies, we evaluate the performance of the two adaptations under various scenarios, including various subgroup and full population effect sizes, the prevalence of subgroups, the number of covariates considered and sample sizes.


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

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