药代动力学取样窗口的构建

IF 0.6 Q4 STATISTICS & PROBABILITY
M. Alam, Nigar Sultana
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引用次数: 0

摘要

本文描述了一种构建药代动力学采样窗口的方法,使其位于最佳时间点附近。在这里,我们考虑的情况是药代动力学(PK)研究伴随着剂量发现研究的I期临床试验。d -最优准则通常用于确定采集血液样本的最佳时间,以便提供有关总体PK参数的最大信息。然而,在d -最优时间点采集血液样本通常是困难的。相反,从合适的时间间隔或窗口中选择采样时间点可以简化这一过程。该方法概念简单,考虑d -最优时间点的平均值和标准差来创建采样窗口。随机时间点可以从这些窗口中选择,然后从下一个队列中收集血液样本。采用非线性随机效应模型对PK数据进行建模。同时,我们采用持续再评估方法对患者进行剂量分配。比较了在d -最优时间点和随机时间点得到的PK参数估计的准确度和精密度。结果足够令人信服,表明所提出的方法是一种有用的血液样本收集工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Construction of Windows for Pharmacokinetic Sampling
This paper describes a method for the construction of pharmacokinetic sampling windows so that they are around the $D$-optimum time points. Here we consider the situation where a pharmacokinetic (PK) study is accompanied by a dose-finding study in phase I clinical trial. The D-optimal criterion is often used to determine the optimal time for collecting blood samples so that they provide maximum information regarding the population PK parameters. However, collecting blood samples at the D-optimal time points is often difficult. Instead, the sampling time point chosen from a suitable time interval or window can ease the process. The proposed method is conceptually simple and considers the average value and standard deviation of D-optimal time points up to create sampling windows. Random time points can be chosen from these windows then to collect blood samples from the next cohort. The nonlinear random-effects model has been used to model the PK data. Also, we employ the continual reassessment method for dose allocation to the patients. Comparisons of the accuracy and precision for the PK parameter estimates obtained at the D-optimal and random time points are also presented. The results are convincing enough to suggest the proposed method as a useful tool for blood sample collection.
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来源期刊
Austrian Journal of Statistics
Austrian Journal of Statistics STATISTICS & PROBABILITY-
CiteScore
1.10
自引率
0.00%
发文量
30
审稿时长
24 weeks
期刊介绍: The Austrian Journal of Statistics is an open-access journal (without any fees) with a long history and is published approximately quarterly by the Austrian Statistical Society. Its general objective is to promote and extend the use of statistical methods in all kind of theoretical and applied disciplines. The Austrian Journal of Statistics is indexed in many data bases, such as Scopus (by Elsevier), Web of Science - ESCI by Clarivate Analytics (formely Thompson & Reuters), DOAJ, Scimago, and many more. The current estimated impact factor (via Publish or Perish) is 0.775, see HERE, or even more indices HERE. Austrian Journal of Statistics ISNN number is 1026597X Original papers and review articles in English will be published in the Austrian Journal of Statistics if judged consistently with these general aims. All papers will be refereed. Special topics sections will appear from time to time. Each section will have as a theme a specialized area of statistical application, theory, or methodology. Technical notes or problems for considerations under Shorter Communications are also invited. A special section is reserved for book reviews.
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