arXiv subjects

Kentaro Takeda

Publications and source records attributed to Kentaro Takeda.

6 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.

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arXiv2026

A staggered seamless dose-optimization design for co-developing monotherapy and combination therapy

Contemporary oncology drug development increasingly requires efficient dose-optimization strategies that evaluate monotherapy (Mono) and combination therapy (Combo) while balancing activity, efficacy, and tolerability. We propose a staggered seamless phase I/II design for settings in which a novel agent is evaluated alone and in combination with an established therapy. In phase I, Mono dose finding begins first, and Combo subtrials can be opened adaptively once a prespecified combination-initiation signal based on early clinical or biological information is observed. Dose assignment uses a model-assisted rule based on toxicity and early activity, with backfilling at tolerable and potentially promising regimens. At the end of phase I, two candidate regimens are selected from the evaluated Mono and Combo regimens using an efficacy-toxicity utility based on accumulated toxicity and treatment-response data. Phase II seamlessly carries forward patients treated at the selected regimens, enrolls additional patients as needed, and applies Bayesian futility and efficacy stopping boundaries to identify a final recommended optimal biological dose (OBD). Simulation studies showed that the proposed design shortened phase I trial duration relative to the comparator designs while maintaining competitive OBD-selection performance and acceptable safety. The seamless phase II component further reduced the need for additional enrollment and supported efficient final OBD selection.

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arXiv2026

A seamless dose-optimization design for monotherapy and combination therapy

The emergence of molecular-targeted agents and immune-oncology therapies has fundamentally transformed oncology drug development, necessitating evolution beyond traditional dose-finding approaches designed for cytotoxic agents. While conventional agents exhibit predictable monotonic dose-response relationships, novel anticancer agents often demonstrate plateau-effect patterns where higher doses may compromise therapeutic benefit, requiring identification of optimal biological doses that balance efficacy and tolerability. The FDA's Project Optimus initiative emphasizes comprehensive dose optimization through parallel randomized cohorts and patient backfilling to better understand pharmacological profiles across multiple dose levels. Contemporary drug development increasingly prioritizes combination therapy alongside monotherapy evaluation, yet existing designs typically assume equivalent roles for both agents, diverging from clinical practice where novel agents combine with established treatments having limited dose options. This paper proposes a seamless dose-optimization design that adaptively evaluates both monotherapy and combination therapy based on efficacy and toxicity outcomes through adaptive subtrials with patient backfilling capabilities. The model-assisted framework employs predetermined Bayesian optimal boundaries, eliminating real-time model fitting while accommodating evaluation of both monotherapy and combination therapy and enabling sequential enrollment with strategic backfilling. Simulation studies demonstrate robust performance across diverse dose-response patterns relevant to contemporary oncology. The design addresses critical gaps between methodological assumptions and clinical reality, offering a practical approach that integrates monotherapy and combination therapy evaluation with efficacy-toxicity-based backfilling.

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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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arXiv2025

BE-BOIN: A Dose Optimization Design Accommodating Backfill and Late-Onset Toxicity

The US Food and Drug Administration (FDA) launched Project Optimus and issued guidance to reform dose-finding and selection trials, shifting the paradigm from identifying the maximum tolerable dose (MTD) to determining the optimal biological dose (OBD), which optimizes the risk and benefit of treatments. The FDA's guidance emphasizes the importance of collecting sufficient toxicity and efficacy data across multiple doses and considering late-onset cumulative toxicity that often results in tolerability issues. To address these challenges, we propose the BE-BOIN (Backfill time-to-Event Bayesian Optimal INterval) design, which allows backfilling patients into safe and effective doses during dose escalation and accommodates late-onset toxicities. BE-BOIN enables the collection of additional safety and efficacy data to enhance the accuracy and reliability of OBD selection and supports real-time dose decisions for new patients. Our simulation studies show that BE-BOIN accurately identifies the MTD and OBD while significantly reducing trial duration.

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arXiv2024

ROMI: A Randomized Two-Stage Basket Trial Design to Optimize Doses for Multiple Indications

Optimizing doses for multiple indications is challenging. The pooled approach of finding a single optimal biological dose (OBD) for all indications ignores that dose-response or dose-toxicity curves may differ between indications, resulting in varying OBDs. Conversely, indication-specific dose optimization often requires a large sample size. To address this challenge, we propose a Randomized two-stage basket trial design that Optimizes doses in Multiple Indications (ROMI). In stage 1, for each indication, response and toxicity are evaluated for a high dose, which may be a previously obtained MTD, with a rule that stops accrual to indications where the high dose is unsafe or ineffective. Indications not terminated proceed to stage 2, where patients are randomized between the high dose and a specified lower dose. A latent-cluster Bayesian hierarchical model is employed to borrow information between indications, while considering the potential heterogeneity of OBD across indications. Indication-specific utilities are used to quantify response-toxicity trade-offs. At the end of stage 2, for each indication with at least one acceptable dose, the dose with highest posterior mean utility is selected as optimal. Two versions of ROMI are presented, one using only stage 2 data for dose optimization and the other optimizing doses using data from both stages. Simulations show that both versions have desirable operating characteristics compared to designs that either ignore indications or optimize dose independently for each indication.

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