Early completion based on adjacent dose information for model-assisted designs to accelerate maximum tolerated dose finding.

IF 1.2 4区 数学
Masahiro Kojima
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引用次数: 0

Abstract

Phase I trials aim to identify the maximum tolerated dose (MTD) early and proceed quickly to an expansion cohort or a Phase II trial to assess the efficacy of the treatment. We present an early completion method based on multiple dosages (adjacent dose information) to accelerate the identification of the MTD in model-assisted designs. By using not only toxicity data for the current dose but also toxicity data for the next higher and lower doses, the MTD can be identified early without compromising accuracy. The early completion method is performed based on dose-assignment probabilities for multiple dosages. These probabilities are straightforward to calculate. We evaluated the early completion method using from an actual clinical trial. In a simulation study, we evaluated the percentage of correct MTD selection and the impact of early completion on trial outcomes. The results indicate that our proposed early completion method maintains a high level of accuracy in MTD selection, with minimal reduction compared to the standard approach. In certain scenarios, the accuracy of MTD selection even improves under the early completion framework. We conclude that the use of this early completion method poses no issue when applied to model-assisted designs.

早期完成基于相邻剂量信息的模型辅助设计,以加速最大耐受剂量的发现。
I期试验旨在尽早确定最大耐受剂量(MTD),并迅速进入扩展队列或II期试验,以评估治疗的疗效。我们提出了一种基于多剂量(相邻剂量信息)的早期完成方法,以加速模型辅助设计中MTD的识别。通过不仅使用当前剂量的毒性数据,而且使用下一个更高和更低剂量的毒性数据,可以在不影响准确性的情况下及早确定MTD。该早期完成方法是基于多个剂量的剂量分配概率来执行的。这些概率很容易计算。我们从实际临床试验中评估了早期完成方法。在模拟研究中,我们评估了正确选择MTD的百分比以及早期完成对试验结果的影响。结果表明,我们提出的早期完井方法在MTD选择方面保持了很高的准确性,与标准方法相比,减少的幅度最小。在某些情况下,在早期完井框架下,MTD选择的准确性甚至有所提高。我们得出的结论是,当应用于模型辅助设计时,使用这种早期完成方法没有问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
International Journal of Biostatistics
International Journal of Biostatistics Mathematics-Statistics and Probability
CiteScore
2.30
自引率
8.30%
发文量
28
期刊介绍: The International Journal of Biostatistics (IJB) seeks to publish new biostatistical models and methods, new statistical theory, as well as original applications of statistical methods, for important practical problems arising from the biological, medical, public health, and agricultural sciences with an emphasis on semiparametric methods. Given many alternatives to publish exist within biostatistics, IJB offers a place to publish for research in biostatistics focusing on modern methods, often based on machine-learning and other data-adaptive methodologies, as well as providing a unique reading experience that compels the author to be explicit about the statistical inference problem addressed by the paper. IJB is intended that the journal cover the entire range of biostatistics, from theoretical advances to relevant and sensible translations of a practical problem into a statistical framework. Electronic publication also allows for data and software code to be appended, and opens the door for reproducible research allowing readers to easily replicate analyses described in a paper. Both original research and review articles will be warmly received, as will articles applying sound statistical methods to practical problems.
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