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1.
Pharm Stat ; 2024 May 20.
Artículo en Inglés | MEDLINE | ID: mdl-38769904

RESUMEN

This paper proposes a trial design for locating group-specific doses when groups are partially or completely ordered by dose sensitivity. Previous trial designs for partially ordered groups are model-based, whereas the proposed method is model-assisted, providing clinicians with a design that is simpler. The proposed method performs similarly to model-based methods, providing simplicity without losing accuracy. Additionally, to the best of our knowledge, the proposed method is the first paper on dose-finding for partially ordered groups with convergence results. To generalize the proposed method, a framework is introduced that allows partial orders to be transferred to a grid format with a known ordering across rows but an unknown ordering within rows.

2.
Contemp Clin Trials ; 136: 107400, 2024 Jan.
Artículo en Inglés | MEDLINE | ID: mdl-38000453

RESUMEN

This paper proposes a phase-I clinical trial design that uses ordinal toxicity to locate group-specific doses when groups are partially or completely ordered prior to the start of the trial. There has been previous work on dose-finding for groups and on dose-finding with ordinal toxicity but a solution to the problem of dose-finding for groups with ordinal toxicity has not been proposed. Simulations compared the proposed method against two methods; one that uses ordinal toxicity but does not use group information and one that uses group information but does not use ordinal toxicity. One issue with the first method is the potential for reversals, when the recommended dose for a more sensitive group is higher than the recommended dose for a less sensitive group. The proposed method avoids reversals, allocates patients to optimal doses more frequently during the trial, and selects optimal doses more frequently at the end of the trial.


Asunto(s)
Proyectos de Investigación , Humanos , Relación Dosis-Respuesta a Droga , Dosis Máxima Tolerada , Simulación por Computador , Teorema de Bayes
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