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June 2017 Subgroup inference for multiple treatments and multiple endpoints in an Alzheimer’s disease treatment trial
Patrick Schnell, Qi Tang, Peter Müller, Bradley P. Carlin
Ann. Appl. Stat. 11(2): 949-966 (June 2017). DOI: 10.1214/17-AOAS1024


Many new experimental treatments outperform the current standard only for a subset of the population. Subgroup identification methods provide estimates for the population subset which benefits most from treatment. However, when more than two treatments and multiple endpoints are under consideration, there are many possible requirements for a particular treatment to be beneficial. In this paper, we adapt notions of decision-theoretic admissibility to the context of evaluating treatments in such trials. As an explicit demonstration of admissibility concepts, we combine our approach with the method of credible subgroups, which in the case of a single outcome and treatment comparison provides Bayesian bounds on the benefiting subpopulation. We investigate our methods’ performance via simulation, and apply them to a recent dataset from an Alzheimer’s disease treatment trial. Our results account for multiplicity while showing patient covariate profiles that are (or are not) likely to be associated with treatment benefit, and are thus useful in their own right or as a guide to patient enrollment in a second stage study.


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Patrick Schnell. Qi Tang. Peter Müller. Bradley P. Carlin. "Subgroup inference for multiple treatments and multiple endpoints in an Alzheimer’s disease treatment trial." Ann. Appl. Stat. 11 (2) 949 - 966, June 2017.


Received: 1 September 2016; Revised: 1 January 2017; Published: June 2017
First available in Project Euclid: 20 July 2017

zbMATH: 06775899
MathSciNet: MR3693553
Digital Object Identifier: 10.1214/17-AOAS1024

Keywords: Bayesian inference , Clinical trials , heterogeneous treatment effect , linear model , simultaneous inference , subgroup identification

Rights: Copyright © 2017 Institute of Mathematical Statistics


Vol.11 • No. 2 • June 2017
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