Background: 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. Due to limited data, identifying the true benefiting population is a challenging task. To tackle this difficulty, the credible subgroups approach provides a pair of bounding subgroups for the true benefiting subgroup, constructed so that one is contained by the benefiting subgroup while the other contains the benefiting subgroup with high probability. However, the method has so far only been developed for parametric linear models. Methods: In this article, we develop the details required to follow the credible subgroups approach in more realistic settings by considering nonlinear and semiparametric regression models, supported for regulatory science by conditional power simulations. We also present an improved multiple testing approach using a step-down procedure. We evaluate our approach via simulations and apply it to data from four trials of Alzheimer’s disease treatments carried out by AbbVie. Results: Semiparametric modeling yields credible subgroups that are more robust to violations of linear treatment effect assumptions, and careful choice of the population of interest as well as the step-down multiple testing procedure result in a higher rate of detection of benefiting types of patients. The approach allows us to identify types of patients that benefit from treatment in the Alzheimer’s disease trials. Conclusion: Attempts to identify benefiting subgroups of patients in clinical trials are often met with skepticism due to a lack of multiplicity control and unrealistically restrictive assumptions. Our proposed approach merges two techniques, credible subgroups, and semiparametric regression, which avoids these problems and makes benefiting subgroup identification practical and reliable.
Bibliographical noteFunding Information:
This work was supported by AbbVie, Inc., the University of Minnesota Doctoral Dissertation Fellowship, and the National Cancer Institute (1-R01-CA157458-01A1 to PMS and BPC). AbbVie contributed to the design, research, interpretation of data, reviewing, and approving of this publication.
© 2017, © The Author(s) 2017.
Copyright 2019 Elsevier B.V., All rights reserved.
- Bayesian inference
- clinical trials
- multiple testing
- personalized medicine
- semiparametric regression
- subgroup identification