arXiv subjects

Hisato Sunami

Publications and source records attributed to Hisato Sunami.

4 recordsLinked to original sources

arXiv2026

Finite-Boundary Reduction and Exact Verification of Strong Familywise Error in Active-Count-Coupled Multi-Arm Efficacy-Toxicity Monitoring

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.

stat.ME↗

arXiv2026

A Globally Calibrated Bayesian Optimal Phase II Design for Adaptive Enrichment Trials

Adaptive enrichment allows development of an experimental treatment to continue when its activity is insufficient in an all-comer population but remains promising in a prespecified biomarker-positive subgroup. However, sequential application of separately calibrated phase II designs can inflate the probability of a false-positive efficacy conclusion. We develop a globally calibrated Bayesian optimal phase II (BOP2) design for branching adaptive enrichment trials. At prespecified all-comer interim analyses, the trial either continues all-comer enrollment or, after crossing the all-comer futility boundary, evaluates the accumulated biomarker-positive data. Enrichment is initiated only when a prespecified minimum number of biomarker-positive patients is available and the biomarker-positive futility boundary is not crossed; otherwise, the trial stops. The all-comer and biomarker-positive thresholds are jointly calibrated for the union of the two possible efficacy claims while accounting for the random subgroup sample size available when enrichment is considered. All decision rules are prespecified before trial initiation. For a binary endpoint, an exact finite-state recursive enumeration enables calibration and operating-characteristic evaluation without Monte Carlo error. Under the prespecified point global null, the proposed design controlled the global type I error rate over the prespecified set of biomarker-positive prevalence values while achieving higher power than the independently calibrated BOP2 comparator across the evaluated alternative scenarios. In the numerical study, the independently calibrated comparator exceeded the nominal global type I error level after its components were embedded in the branching procedure. The framework is also extended to complex categorical endpoints using a Dirichlet--multinomial formulation, with calibration and evaluation performed by simulation.

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arXiv2026

Hybrid Non-informative and Informative Prior Model-assisted Designs for Mid-trial Dose Insertion

In oncology phase I trials, model-assisted designs have been increasingly adopted because they enable adaptive yet operationally simple dose adjustment based on accumulating safety data, leading to a paradigm shift in dose-escalation methodology. In practice, a single mid-trial dose insertion may be considered to examine safer doses and/or to collect more informative efficacy data. In this study, we investigate methods to improve dose assignment and the selection of the maximum tolerated dose (MTD) or the optimal biological dose (OBD) when a new dose level is added during an ongoing trial under a model-assisted framework, by assigning informative prior information to the inserted dose. We propose a hybrid design that uses a non-informative model-assisted design at trial initiation and, upon dose insertion, applies an informative-prior extension only to the newly added dose. In addition, to address potential skeleton misspecification, we propose two adaptive extensions: (i) an online-weighting approach that updates the skeleton over time, and (ii) a Bayesian-mixture approach that robustly combines multiple candidate skeletons. We evaluate the proposed methods through simulation studies.

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arXiv2024

Semiparametric Piecewise Accelerated Failure Time Model for the Analysis of Immune-Oncology Clinical Trials

Effectiveness of immune-oncology chemotherapies has been presented in recent clinical trials. The Kaplan-Meier estimates of the survival functions of the immune therapy and the control often suggested the presence of the lag-time until the immune therapy began to act. It implies the use of hazard ratio under the proportional hazards assumption would not be appealing, and many alternatives have been investigated such as the restricted mean survival time. In addition to such overall summary of the treatment contrast, the lag-time is also an important feature of the treatment effect. Identical survival functions up to the lag-time implies patients who are likely to die before the lag-time would not benefit the treatment and identifying such patients would be very important. We propose the semiparametric piecewise accelerated failure time model and its inference procedure based on the semiparametric maximum likelihood method. It provides not only an overall treatment summary, but also a framework to identify patients who have less benefit from the immune-therapy in a unified way. Numerical experiments confirm that each parameter can be estimated with minimal bias. Through a real data analysis, we illustrate the evaluation of the effect of immune-oncology therapy and the characterization of covariates in which patients are unlikely to receive the benefit of treatment.

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