Online Program Home
  My Program

Abstract Details

Activity Number: 28 - Personalized Medicine
Type: Contributed
Date/Time: Sunday, July 30, 2017 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract #322413 View Presentation
Title: Semiparametric Benefiting Subgroup Identification via Credible Subgroups
Author(s): Patrick Schnell* and Qi Tang and Peter Mueller and Brad Carlin
Companies: University of Minnesota and AbbVie, Inc. and UT Austin and University of Minnesota
Keywords: Bayesian inference ; clinical trials ; personalized medicine ; semiparametric regression ; multiple testing ; subgroup identification
Abstract:

A recent focus in the 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 constructed so that it is likely that one contains only patients who benefit and the other contains all patients who benefit, but the method has so far only been developed for linear models. In this paper we develop the details required to follow the credible subgroups approach in more realistic settings by considering semiparametric regression models, conditional power simulations, and improved multiple testing through a step-down procedure. We illustrate our approach using data from four recent trials of Alzheimer's disease treatments.


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

Back to the full JSM 2017 program

 
 
Copyright © American Statistical Association