基于量化目标的分组识别。

IF 1.3 4区 医学 Q4 PHARMACOLOGY & PHARMACY Pharmaceutical Statistics Pub Date : 2024-11-17 DOI:10.1002/pst.2455
Yan Sun, A S Hedayat
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引用次数: 0

摘要

精准医疗是药物开发的未来,而亚组识别在实现这一目标的过程中起着至关重要的作用。在本文中,我们提出了一种功能强大的端到端解决方案 squant(可在 CRAN 上获取),用于探索一系列定量目标。该方法将原始研究转换为人工 1:1 随机试验,具有灵活的目标函数、可解释性良好的稳定特征以及嵌入式误诊率 (FDR) 控制。我们通过模拟演示了该方法的性能,并提供了一个真实数据示例。
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Subgroup Identification Based on Quantitative Objectives.

Precision medicine is the future of drug development, and subgroup identification plays a critical role in achieving the goal. In this paper, we propose a powerful end-to-end solution squant (available on CRAN) that explores a sequence of quantitative objectives. The method converts the original study to an artificial 1:1 randomized trial, and features a flexible objective function, a stable signature with good interpretability, and an embedded false discovery rate (FDR) control. We demonstrate its performance through simulation and provide a real data example.

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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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