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Activity Number: 464 - New Directions in Personalized Treatment Selection
Type: Topic Contributed
Date/Time: Wednesday, August 1, 2018 : 8:30 AM to 10:20 AM
Sponsor: International Indian Statistical Association
Abstract #327252
Title: Multiplicity-Controlled Benefiting Subgroup Identification via Credible Subgroups
Author(s): Patrick Schnell* and Qi Tang and Peter Müller and Brad Carlin
Companies: Ohio State University and Sanofi and University of Texas Austin and University of Minnesota
Keywords: Bayesian inference; multiple testing; personalized medicine; subgroup identification

A recent focus in health sciences has been the development of personalized medicine, which includes determining the population for which a given treatment is effective. The credible subgroups approach provides a pair of bounding subgroups for the benefiting subgroup in covariate space, constructed so that it is likely that one contains the benefiting subgroup and the other is entirely contained by it. This approach fully controls for the multiplicity inherent in testing for benefit at every covariate point, and does not require pre-specification of subgroups. We illustrate the approach in linear and semiparametric regression settings using data from trials of Alzheimer's disease treatments.

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

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