Finite-Boundary Reduction and Exact Verification of Strong Familywise Error in Active-Count-Coupled Multi-Arm Efficacy-Toxicity Monitoring
Abstract
Randomized dose-optimization trials may screen several candidate doses using binary efficacy and toxicity outcomes. A dose is inadmissible if efficacy is insufficient or toxicity is excessive, so each dose-specific null hypothesis is a union region and strong familywise error control must hold across arbitrary mixtures of inadmissible and promising doses. We study exact verification of multistage monitoring rules in which armwise decisions may be coupled through the number of active arms remaining at each interim analysis. For uncoupled rules, we derive an exact product representation and show that a complete-null boundary configuration is least favourable. Under active-count coupling, this factorization no longer holds because a promising dose can remain active and alter future boundaries for null doses. Assuming independent sampling across arms, prespecified per-dose analysis schedules, analysis timing not driven by the observed efficacy or toxicity outcomes, arm-specific monitoring statistics, and stagewise monotonicity, we establish an exact finite-boundary characterization without parametric restrictions on the within-arm joint efficacy-toxicity distribution. Each dose need only be evaluated at an efficacy-null boundary, a toxicity-null boundary, or a maximally favourable alternative. A deterministic finite-state recursion then verifies a fixed decision table without Monte Carlo error. Numerical studies confirmed the prespecified strong familywise error control and showed that active-count coupling can shift the least-favourable configuration away from the complete null while improving joint retention of multiple promising doses. A published randomized dose-ranging trial was used as a clinical illustration of how the framework could be prospectively implemented. The framework separates monitoring-rule construction from rigorous error verification.
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BibTeX
@misc{kojima2026finiteboundary,
title = {{Finite-Boundary Reduction and Exact Verification of Strong Familywise Error in Active-Count-Coupled Multi-Arm Efficacy-Toxicity Monitoring}},
author = {Masahiro Kojima and Hisato Sunami and Kentaro Takeda},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.06886},
eprint = {2609.06886},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.06886},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Finite-Boundary Reduction and Exact Verification of Strong Familywise Error in Active-Count-Coupled Multi-Arm Efficacy-Toxicity Monitoring
AU - Masahiro Kojima
AU - Hisato Sunami
AU - Kentaro Takeda
PY - 2026
DA - 2026/09/07
PB - arXiv
DO - 10.48550/arxiv.2609.06886
UR - https://arxiv.org/abs/2609.06886
AB - Randomized dose-optimization trials may screen several candidate doses using binary efficacy and toxicity outcomes. A dose is inadmissible if efficacy is insufficient or toxicity is excessive, so each dose-specific null hypothesis is a union region and strong familywise error control must hold across arbitrary mixtures of inadmissible and promising doses. We study exact verification of multistage monitoring rules in which armwise decisions may be coupled through the number of active arms remaining at each interim analysis. For uncoupled rules, we derive an exact product representation and show that a complete-null boundary configuration is least favourable. Under active-count coupling, this factorization no longer holds because a promising dose can remain active and alter future boundaries for null doses. Assuming independent sampling across arms, prespecified per-dose analysis schedules, analysis timing not driven by the observed efficacy or toxicity outcomes, arm-specific monitoring statistics, and stagewise monotonicity, we establish an exact finite-boundary characterization without parametric restrictions on the within-arm joint efficacy-toxicity distribution. Each dose need only be evaluated at an efficacy-null boundary, a toxicity-null boundary, or a maximally favourable alternative. A deterministic finite-state recursion then verifies a fixed decision table without Monte Carlo error. Numerical studies confirmed the prespecified strong familywise error control and showed that active-count coupling can shift the least-favourable configuration away from the complete null while improving joint retention of multiple promising doses. A published randomized dose-ranging trial was used as a clinical illustration of how the framework could be prospectively implemented. The framework separates monitoring-rule construction from rigorous error verification.
ER -