Early completion of phase I cancer clinical trials with Bayesian optimal interval design

Kojima, Masahiro

doi:10.1002/sim.8886

Cited by 11 publications

(6 citation statements)

References 29 publications

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“…In conclusion, Bayesian optimal design addresses a fundamental problem in science: the intelligent collection of data resulting in greater information efficiency, reduced sampling cost and improved estimation. Such benefits have been observed in clinical trials [19][20][21][22] and environmental monitoring [15,23], and we have shown how they can be used to offer flexible yet efficient sampling in a real-world context. One limitation of the approach is the potential reliance of designs on a number of assumptions, e.g.…”

Section: (B) Case Study: Sampling Windows For Coral Reef Monitoringmentioning

confidence: 70%

Being Bayesian in the 2020s: opportunities and challenges in the practice of modern applied Bayesian statistics

Bon

Bretherton

Buchhorn

et al. 2023

Phil. Trans. R. Soc. A.

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Building on a strong foundation of philosophy, theory, methods and computation over the past three decades, Bayesian approaches are now an integral part of the toolkit for most statisticians and data scientists. Whether they are dedicated Bayesians or opportunistic users, applied professionals can now reap many of the benefits afforded by the Bayesian paradigm. In this paper, we touch on six modern opportunities and challenges in applied Bayesian statistics: intelligent data collection, new data sources, federated analysis, inference for implicit models, model transfer and purposeful software products. This article is part of the theme issue ‘Bayesian inference: challenges, perspectives, and prospects’.

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Section: (B) Case Study: Sampling Windows For Coral Reef Monitoringmentioning

confidence: 70%

Being Bayesian in the 2020s: opportunities and challenges in the practice of modern applied Bayesian statistics

Bon

Bretherton

Buchhorn

et al. 2023

Phil. Trans. R. Soc. A.

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“…Although the BOIN and Keyboard designs have a stopping rule that can be used to terminate the trial if the number of patients treated exceeds the predetermined number, there is no statistical basis for the predetermined number. Kojima [35][36][37] proposed an early completion method for model-assisted designs but did not discuss the applicability of this method for dose combination trials.…”

Section: Introductionmentioning

confidence: 99%

Data-dependent early completion of dose-finding trials for drug-combination

Kojima

2023

Stat Methods Med Res

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I propose a data-dependent early completion of dose-finding trials for drug combinations. Early completion is determined when the dose retainment probability using both the trial data and the number of remaining patients is high. An early completion method in which the dose retainment probability is adjusted by a bivariate isotonic regression is also proposed. Early completion is demonstrated for a virtual trial. The performance of the early completion method is evaluated by simulation studies with 12 scenarios. I have shown that, compared with non-early completion designs, the proposed early completion methods reduce the number of patients treated while maintaining similar performance. The number of patients for determining early completion before a trial start is determined and the program code for calculating the dose retainment probability is provided. Abstract Purpose Model-assisted designs for drug combination trials have been proposed as novel designs with simple and superior performance. However, model-assisted designs have the disadvantage that the sample size must be set in advance, and trials cannot be completed until the number of patients treated reaches the pre-set sample size. Model-assisted designs have a stopping rule that can be used to terminate the trial if the number of patients treated exceeds the predetermined number, there is no statistical basis for the predetermined number. Here, I propose two methods for data-dependent early completion of dose-finding trials for drug combination: (1) an early completion method based on dose retainment probability, and (2) an early completion method in which the dose retainment probability is adjusted by a bivariate isotonic regression. Methods Early completion is determined when the dose retainment probability using both trial data and the number of remaining patients is high. Early completion of a virtual trial was demonstrated. The performances of the early completion methods were evaluated by simulation studies with 12 scenarios. Results The simulation studies showed that the percentage of early completion was an average of approximately 70%, and the number of patients treated was 25% less than the planned sample size. The percentage of correct maximum tolerated dose combination selection for the early completion methods was similar to that of non-early completion methods with an average difference of approximately 3%. Conclusion The performance of the proposed early completion methods was similar to that of the non-early completion methods. Furthermore, the number of patients for determining early completion before the trial starts was determined and a program code for calculating the dose retainment probability was proposed.

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“…However, the study cannot identify the MTD until the number of patients treated reaches the sample size. Early identification methods for MTD have been proposed [ 4 , 5 ]. However, the methods cannot apply to TITE model-assisted designs.…”

Section: Introductionmentioning

confidence: 99%

Adaptive design for identifying maximum tolerated dose early to accelerate dose-finding trial

Kojima

2022

BMC Med Res Methodol

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Purpose The early identification of maximum tolerated dose (MTD) in phase I trial leads to faster progression to a phase II trial or an expansion cohort to confirm efficacy. Methods We propose a novel adaptive design for identifying MTD early to accelerate dose-finding trials. The early identification of MTD is determined adaptively by dose-retainment probability using a trial data via Bayesian analysis. We applied the early identification design to an actual trial. A simulation study evaluates the performance of the early identification design. Results In the actual study, we confirmed the MTD could be early identified and the study period was shortened. In the simulation study, the percentage of the correct MTD selection in the early identification Keyboard and early identification Bayesian optimal interval (BOIN) designs was almost same from the non-early identification version. The early identification Keyboard and BOIN designs reduced the study duration by about 50% from the model-assisted designs. In addition, the early identification Keyboard and BOIN designs reduced the study duration by about 20% from time-to-event model-assisted designs. Conclusion We proposed the early identification of MTD maintaining the accuracy to be able to short the study period.

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Early completion of phase I cancer clinical trials with Bayesian optimal interval design

Cited by 11 publications

References 29 publications

Being Bayesian in the 2020s: opportunities and challenges in the practice of modern applied Bayesian statistics

Being Bayesian in the 2020s: opportunities and challenges in the practice of modern applied Bayesian statistics

Data-dependent early completion of dose-finding trials for drug-combination

Adaptive design for identifying maximum tolerated dose early to accelerate dose-finding trial

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