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Subgroup identification in dose-finding trials via model-based recursive partitioning


Thomas, Marius; Bornkamp, Björn; Seibold, Heidi (2018). Subgroup identification in dose-finding trials via model-based recursive partitioning. Statistics in Medicine, 37(10):1608-1624.

Abstract

An important task in early‐phase drug development is to identify patients, which respond better or worse to an experimental treatment. While a variety of different subgroup identification methods have been developed for the situation of randomized clinical trials that study an experimental treatment and control, much less work has been done in the situation when patients are randomized to different dose groups. In this article, we propose new strategies to perform subgroup analyses in dose‐finding trials and discuss the challenges, which arise in this new setting. We consider model‐based recursive partitioning, which has recently been applied to subgroup identification in 2‐arm trials, as a promising method to tackle these challenges and assess its viability using a real trial example and simulations. Our results show that model‐based recursive partitioning can be used to identify subgroups of patients with different dose‐response curves and improves estimation of treatment effects and minimum effective doses compared to models ignoring possible subgroups, when heterogeneity among patients is present.

Abstract

An important task in early‐phase drug development is to identify patients, which respond better or worse to an experimental treatment. While a variety of different subgroup identification methods have been developed for the situation of randomized clinical trials that study an experimental treatment and control, much less work has been done in the situation when patients are randomized to different dose groups. In this article, we propose new strategies to perform subgroup analyses in dose‐finding trials and discuss the challenges, which arise in this new setting. We consider model‐based recursive partitioning, which has recently been applied to subgroup identification in 2‐arm trials, as a promising method to tackle these challenges and assess its viability using a real trial example and simulations. Our results show that model‐based recursive partitioning can be used to identify subgroups of patients with different dose‐response curves and improves estimation of treatment effects and minimum effective doses compared to models ignoring possible subgroups, when heterogeneity among patients is present.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Epidemiology, Biostatistics and Prevention Institute (EBPI)
Dewey Decimal Classification:610 Medicine & health
Uncontrolled Keywords:Statistics and Probability, Epidemiology
Language:English
Date:2018
Deposited On:24 Apr 2018 10:15
Last Modified:24 Sep 2019 23:27
Publisher:Wiley-Blackwell Publishing, Inc.
ISSN:0277-6715
OA Status:Green
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.1002/sim.7594
Related URLs:https://profiles.impactstory.org/u/0000-0002-8960-9642 (Author)
PubMed ID:29388228
Project Information:
  • : FunderH2020
  • : Grant ID633567
  • : Project TitleIDEAS - Improving Design, Evaluation and Analysis of early drug development Studies

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