Phase I/<scp>II</scp> Design for Selecting Subgroup‐Specific Optimal Biological Doses for Prespecified Subgroups

We propose a phase I/II trial design to support dose-finding when the optimal biological dose (OBD) may differ in two prespecified patient subgroups. The proposed design uses a utility function to quantify efficacy-toxicity trade-offs, and a Bayesian model with spike and slab prior distributions for the subgroup effect on toxicity and efficacy to guide dosing and to facilitate identifying either subgroup-specific OBDs or a common OBD depending on the resulting trial data. In a simulation study, we find the proposed design performs nearly as well as a design that ignores subgroups when the dose-toxicity and dose-efficacy relationships are the same in both subgroups, and nearly as well as a design with independent dose-finding within each subgroup when these relationships differ across subgroups. In other words, the proposed adaptive design performs similarly to the design that would be chosen if investigators possessed foreknowledge about whether the dose-toxicity and/or dose-efficacy relationship differs across two prespecified subgroups. Thus, the proposed design may be effective for OBD selection when uncertainty exists about whether the OBD differs in two prespecified subgroups.

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Publication Details

Journal
Statistics in Medicine
Published
2024-10-18
DOI
https://doi.org/10.1002/sim.10256
Citations
2
Primary Topic
Statistical Methods in Clinical Trials
Type
article
Field-Weighted Citation Impact
1.42

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article

Phase I/II Design for Selecting Subgroup‐Specific Optimal Biological Doses for Prespecified Subgroups

Thomas A. Murray, Anne Eaton, Scott P. Porter
2 citations
Statistics in Medicine
Statistical Methods in Clinical Trials
1.42
article

Phase I/II Design for Selecting Subgroup‐Specific Optimal Biological Doses for Prespecified Subgroups

Thomas A. Murray, Anne Eaton, Scott P. Porter
article en
2 citations

Abstract

We propose a phase I/II trial design to support dose-finding when the optimal biological dose (OBD) may differ in two prespecified patient subgroups. The proposed design uses a utility function to quantify efficacy-toxicity trade-offs, and a Bayesian model with spike and slab prior distributions for the subgroup effect on toxicity and efficacy to guide dosing and to facilitate identifying either subgroup-specific OBDs or a common OBD depending on the resulting trial data. In a simulation study, we find the proposed design performs nearly as well as a design that ignores subgroups when the dose-toxicity and dose-efficacy relationships are the same in both subgroups, and nearly as well as a design with independent dose-finding within each subgroup when these relationships differ across subgroups. In other words, the proposed adaptive design performs similarly to the design that would be chosen if investigators possessed foreknowledge about whether the dose-toxicity and/or dose-efficacy relationship differs across two prespecified subgroups. Thus, the proposed design may be effective for OBD selection when uncertainty exists about whether the OBD differs in two prespecified subgroups.

Statistics in MedicineVol. 43(28)
University of Minnesota (US)
University of Minnesota, National Institutes of Health
Openalex Percentile: Top 17%
Statistical Methods in Clinical Trials
1.42
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