Controlled amplification in oncology dose-finding trials

Dehbi, Hakim‐Moulay; O’Quigley, John; Iasonos, Alexia

doi:10.1016/j.cct.2022.107021

Cited by 5 publications

(2 citation statements)

References 24 publications

(55 reference statements)

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“…4–8 Recent publications have suggested to guide the escalation decisions based on toxicity only and incorporate the efficacy and pharmacokinetics/pharmacodynamics markers at study end to choose the OBD dose. 9,10 However, the majority of phase I oncology trials are still driven by a single binary toxicity endpoint, with the MTD being the primary trial objective. 11 Various dose-finding methods have been proposed for designing phase I trials.…”

Section: Introductionmentioning

confidence: 99%

The 3 + 3 design in dose-finding studies with small sample sizes: Pitfalls and possible remedies

Chiuzan,

Dehbi

2024

Clinical Trials

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In the last few years, numerous novel designs have been proposed to improve the efficiency and accuracy of phase I trials to identify the maximum-tolerated dose (MTD) or the optimal biological dose (OBD) for noncytotoxic agents. However, the conventional 3+3 approach, known for its and poor performance, continues to be an attractive choice for many trials despite these alternative suggestions. The article seeks to underscore the importance of moving beyond the 3+3 design by highlighting a different key element in trial design: the estimation of sample size and its crucial role in predicting toxicity and determining the MTD. We use simulation studies to compare the performance of the most used phase I approaches: 3+3, Continual Reassessment Method (CRM), Keyboard and Bayesian Optimal Interval (BOIN) designs regarding three key operating characteristics: the percentage of correct selection of the true MTD, the average number of patients allocated per dose level, and the average total sample size. The simulation results consistently show that the 3+3 algorithm underperforms in comparison to model-based and model-assisted designs across all scenarios and metrics. The 3+3 method yields significantly lower (up to three times) probabilities in identifying the correct MTD, often selecting doses one or even two levels below the actual MTD. The 3+3 design allocates significantly fewer patients at the true MTD, assigns higher numbers to lower dose levels, and rarely explores doses above the target dose-limiting toxicity (DLT) rate. The overall performance of the 3+3 method is suboptimal, with a high level of unexplained uncertainty and significant implications for accurately determining the MTD. While the primary focus of the article is to demonstrate the limitations of the 3+3 algorithm, the question remains about the preferred alternative approach. The intention is not to definitively recommend one model-based or model-assisted method over others, as their performance can vary based on parameters and model specifications. However, the presented results indicate that the CRM, Keyboard, and BOIN designs consistently outperform the 3+3 and offer improved efficiency and precision in determining the MTD, which is crucial in early-phase clinical trials.

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Section: Introductionmentioning

confidence: 99%

The 3 + 3 design in dose-finding studies with small sample sizes: Pitfalls and possible remedies

Chiuzan,

Dehbi

2024

Clinical Trials

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“…Various methods have been proposed to balance the risk‐benefit trade‐offs, such as EffTox, 12 BOIN12, 13 and those based on backfill designs 14,15 . Following the introduction of Project Optimus, there has been a surge in innovative and practical methodologies aiming to integrate dose optimization with seamless design frameworks, such as the MATS design, 16 the DROID design, 17 the controlled amplification approach, 18 and the Bi3 + 3 design 19 for early‐phase studies. For late‐phase development, integrating seamless Phase II/III design with dose optimization has emerged as an intriguing area of research.…”

Section: Introductionmentioning

confidence: 99%

A seamless phase II/III design with dose optimization for oncology drug development

Li,

Zhang,

et al. 2024

Statistics in Medicine

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The US FDA's Project Optimus initiative that emphasizes dose optimization prior to marketing approval represents a pivotal shift in oncology drug development. It has a ripple effect for rethinking what changes may be made to conventional pivotal trial designs to incorporate a dose optimization component. Aligned with this initiative, we propose a novel seamless phase II/III design with dose optimization (SDDO framework). The proposed design starts with dose optimization in a randomized setting, leading to an interim analysis focused on optimal dose selection, trial continuation decisions, and sample size re‐estimation (SSR). Based on the decision at interim analysis, patient enrollment continues for both the selected dose arm and control arm, and the significance of treatment effects will be determined at final analysis. The SDDO framework offers increased flexibility and cost‐efficiency through sample size adjustment, while stringently controlling the Type I error. This proposed design also facilitates both accelerated approval (AA) and regular approval in a “one‐trial” approach. Extensive simulation studies confirm that our design reliably identifies the optimal dosage and makes preferable decisions with a reduced sample size while retaining statistical power.

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Implementing and assessing Bayesian response-adaptive randomisation for backfilling in dose-finding trials

Pin,

Villar,

Dehbi

2024

Contemporary Clinical Trials

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Controlled amplification in oncology dose-finding trials

Cited by 5 publications

References 24 publications

The 3 + 3 design in dose-finding studies with small sample sizes: Pitfalls and possible remedies

The 3 + 3 design in dose-finding studies with small sample sizes: Pitfalls and possible remedies

A seamless phase II/III design with dose optimization for oncology drug development

Implementing and assessing Bayesian response-adaptive randomisation for backfilling in dose-finding trials

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