Dose individualization and variable selection by using the Bayesian lasso in early phase dose finding trials

Yasuyuki Kakurai; Shuhei Kaneko; Chikuma Hamada; Akihiro Hirakawa

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Dose individualization and variable selection by using the Bayesian lasso in early phase dose finding trials

机译：在早期剂量寻找试验中使用贝叶斯套索进行剂量个体化和变量选择

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摘要

Recommended phase 2 doses for some drugs may differ according to a patient's clinical or genetic characteristics. We develop a new method that determines the individualized optimal dose according to patterns of patient covariates and selects the covariates that are associated with efficacy and toxicity in early phase trials for evaluating multiple patient covariates of interest. To address the difficulty of high dimensional estimation of model parameters with a limited sample size, we propose the use of the Bayesian least absolute shrinkage and selection operator, which is a penalized regression approach. We demonstrate the potential utility of this proposed method through various simulation studies.

机译：根据患者的临床或遗传特征，某些药物的推荐2期剂量可能有所不同。我们开发了一种新方法，可以根据患者协变量的模式确定个体化的最佳剂量，并在早期试验中选择与疗效和毒性相关的协变量，以评估多个目标患者协变量。为了解决在样本量有限的情况下对模型参数进行高维估计的困难，我们建议使用贝叶斯最小绝对收缩和选择算子，这是一种惩罚性回归方法。我们通过各种仿真研究证明了该方法的潜在实用性。

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来源
《Journal of the royal statistical society》 |2019年第2期|445-460|共16页
作者
Yasuyuki Kakurai; Shuhei Kaneko; Chikuma Hamada; Akihiro Hirakawa;
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作者单位

Daiichi-Sankyo and Tokyo University of Science, Tokyo, Japan;

Novartis Pharma, Tokyo, Japan;

Tokyo University of Science, Japan;

University of Tokyo, Japan;

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原文格式 PDF
正文语种 eng
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关键词
Bayesian lasso; Dose finding; Dose individualization; Patient covariate pattern; Phase Ⅰ trials;

机译：贝叶斯套索;剂量发现;剂量个体化;患者协变量模式;Ⅰ期试验;

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1. Bayesian dose‐finding phase I trial design incorporating historical data from a preceding trial [J] . Takeda Kentaro, Morita Satoshi Pharmaceutical statistics. . 2018,第4期

机译：贝叶斯剂量查找阶段I试验设计纳入前试验中的历史数据
2. dfpk: An R-package for Bayesian dose-finding designs using pharmacokinetics (PK) for phase I clinical trials [J] . Toumazi A., Comets E., Alberti C., Computer Methods and Programs in Biomedicine: An International Journal Devoted to the Development, Implementation and Exchange of Computing Methodology and Software Systems in Biomedical Research and Medical Practice . 2018,第期

机译：DFPK：使用药代动力学（PK）的贝叶斯剂量查找设计的R-Package用于I阶段临床试验
3. Uniformly most powerful Bayesian interval design for phase I dose‐finding trials [J] . Lin Ruitao, Yin Guosheng Pharmaceutical statistics. . 2018,第6期

机译：均匀地最强大的贝叶斯间隔设计，用于阶段i剂量查找试验
4. Using a Bayesian Feature-selection Algorithm to Identify Dose-response Models Based on the Shape of the 3D Dose-distribution: An Example from a Head-and-neck Cancer Trial [C] . Buettner Florian, Gulliford Sarah L., Webb Steve, Ninth International Conference on Machine Learning and Applications . 2010

机译：使用贝叶斯特征选择算法识别基于3D剂量分布形状的剂量反应模型：以头颈癌试验为例
5. An adaptive Bayesian approach to jointly modeling response and toxicity in phase I dose-finding trials. [D] . Wang, Meihua. 2007

机译：自适应贝叶斯方法在第一阶段剂量寻找试验中共同模拟反应和毒性。
6. Dose selection for glycopyrrolate/eFlow® phase III clinical studies: results from GOLDEN (Glycopyrrolate for Obstructive Lung Disease via Electronic Nebulizer) phase II dose-finding studies [O] . James F. Donohue, Thomas Goodin, Robert Tosiello, 2017

机译：格隆溴铵/eFlow®III期临床研究的剂量选择：GOLDEN（通过电子雾化器用于阻塞性肺疾病的格隆溴铵）II期剂量研究的结果
7. A Bayesian phase 2 model based adaptive design to optimise antivenom dosing: application to a dose-finding trial for a novel Russell’s viper antivenom in Myanmar [O] . James A Watson, Thomas Lamb, Jane Holmes, 2020

机译：基于贝叶斯期2模型的适应性设计，优化抗血液给药：在缅甸新型罗素毒蛇抗鹿毒蛇毒品的应用中的应用

Dose individualization and variable selection by using the Bayesian lasso in early phase dose finding trials

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