使用部分聚类虚弱模型估算具有多种治疗方法的生物标记物策略设计的样本量。

IF 1.3 4区 医学 Q4 PHARMACOLOGY & PHARMACY Pharmaceutical Statistics Pub Date : 2024-07-16 DOI:10.1002/pst.2407
Derek Dinart, Virginie Rondeau, Carine Bellera
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引用次数: 0

摘要

生物标志物指导疗法是一个不断发展的医学研究领域。为了优化生物标记物的使用,人们提出了包括生物标记物策略设计(BSD)在内的多种研究设计。与传统设计不同的是,这里的重点是比较治疗策略,而不是治疗分子本身。患者被分配到基于生物标记物的策略(BBS)组或非基于生物标记物的策略(NBBS)组,在BBS组中,生物标记物阳性患者接受针对已确定生物标记物的实验性治疗;在NBBS组中,患者无论其生物标记物状态如何都接受治疗。我们提出了一种基于部分聚类虚弱模型(PCFM)的模拟方法以及 Freidlin 公式的扩展,用于估算采用多种靶向治疗的 BSD 所需的样本量。样本量主要受治疗效果异质性、生物标志物阴性患者比例和随机化比例的影响。PCFM 非常适合数据结构,是传统方法的替代方案。
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Sample Size Estimation Using a Partially Clustered Frailty Model for Biomarker-Strategy Designs With Multiple Treatments.

Biomarker-guided therapy is a growing area of research in medicine. To optimize the use of biomarkers, several study designs including the biomarker-strategy design (BSD) have been proposed. Unlike traditional designs, the emphasis here is on comparing treatment strategies and not on treatment molecules as such. Patients are assigned to either a biomarker-based strategy (BBS) arm, in which biomarker-positive patients receive an experimental treatment that targets the identified biomarker, or a non-biomarker-based strategy (NBBS) arm, in which patients receive treatment regardless of their biomarker status. We proposed a simulation method based on a partially clustered frailty model (PCFM) as well as an extension of Freidlin formula to estimate the sample size required for BSD with multiple targeted treatments. The sample size was mainly influenced by the heterogeneity of treatment effect, the proportion of biomarker-negative patients, and the randomization ratio. The PCFM is well suited for the data structure and offers an alternative to traditional methodologies.

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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.
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