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

Marius Thomas1, Björn Bornkamp1, Heidi Seibold2

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|February 2, 2018
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Summary
This summary is machine-generated.

Identifying patient subgroups with varying responses to experimental treatments is crucial in drug development. This study introduces novel subgroup analysis strategies for dose-finding trials, improving treatment effect and minimum effective dose estimation.

Keywords:
dose estimationnonlinear modelspersonalized medicineregression trees

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Area of Science:

  • Pharmacometrics
  • Clinical Trial Design
  • Biostatistics

Background:

  • Identifying patient subgroups that respond differently to experimental treatments is vital in early-phase drug development.
  • Existing subgroup identification methods are primarily designed for two-arm randomized clinical trials, with less focus on dose-finding trials.

Purpose of the Study:

  • To propose and evaluate new strategies for subgroup analyses specifically within dose-finding clinical trials.
  • To address the unique challenges presented by subgroup identification in settings with multiple dose randomization.

Main Methods:

  • Utilizing model-based recursive partitioning, a method previously applied to two-arm trials.
  • Assessing the viability of this method through simulations and a real-world clinical trial example.

Main Results:

  • Model-based recursive partitioning effectively identifies patient subgroups with distinct dose-response curves.
  • This approach enhances the estimation of treatment effects and minimum effective doses when patient heterogeneity is present.

Conclusions:

  • Model-based recursive partitioning is a viable and effective method for subgroup analysis in dose-finding trials.
  • This technique improves upon models that do not account for patient heterogeneity, leading to more precise drug development insights.