部分或完全有序群体的模型辅助设计。

IF 1.3 4区 医学 Q4 PHARMACOLOGY & PHARMACY Pharmaceutical Statistics Pub Date : 2024-05-20 DOI:10.1002/pst.2396
Connor Celum, Mark Conaway
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引用次数: 0

摘要

本文提出了一种试验设计方法,用于在按剂量敏感性部分或完全排序的组别中定位特定组别的剂量。以往针对部分排序组的试验设计是基于模型的,而本文提出的方法是模型辅助的,为临床医生提供了一种更简单的设计。所提出的方法与基于模型的方法性能相似,既简单又不失准确性。此外,据我们所知,所提出的方法是首篇关于部分有序分组剂量计算的论文,并给出了收敛结果。为了推广所提出的方法,我们引入了一个框架,允许将部分排序转移到网格格式中,网格中各行的排序是已知的,但各行内部的排序是未知的。
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A model-assisted design for partially or completely ordered groups.

This paper proposes a trial design for locating group-specific doses when groups are partially or completely ordered by dose sensitivity. Previous trial designs for partially ordered groups are model-based, whereas the proposed method is model-assisted, providing clinicians with a design that is simpler. The proposed method performs similarly to model-based methods, providing simplicity without losing accuracy. Additionally, to the best of our knowledge, the proposed method is the first paper on dose-finding for partially ordered groups with convergence results. To generalize the proposed method, a framework is introduced that allows partial orders to be transferred to a grid format with a known ordering across rows but an unknown ordering within rows.

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来源期刊
Pharmaceutical Statistics
Pharmaceutical Statistics 医学-统计学与概率论
CiteScore
2.70
自引率
6.70%
发文量
90
审稿时长
6-12 weeks
期刊介绍: Pharmaceutical Statistics is an industry-led initiative, tackling real problems in statistical applications. The Journal publishes papers that share experiences in the practical application of statistics within the pharmaceutical industry. It covers all aspects of pharmaceutical statistical applications from discovery, through pre-clinical development, clinical development, post-marketing surveillance, consumer health, production, epidemiology, and health economics. The Journal is both international and multidisciplinary. It includes high quality practical papers, case studies and review papers.
期刊最新文献
Beyond the Fragility Index. A Model-Based Trial Design With a Randomization Scheme Considering Pharmacokinetics Exposure for Dose Optimization in Oncology. Potential Bias Models With Bayesian Shrinkage Priors for Dynamic Borrowing of Multiple Historical Control Data. Subgroup Identification Based on Quantitative Objectives. A Bayesian Dynamic Model-Based Adaptive Design for Oncology Dose Optimization in Phase I/II Clinical Trials.
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