Combination MCP-Mod for two-drug combination dose-ranging studies.

IF 1.2 4区 医学 Q4 PHARMACOLOGY & PHARMACY Journal of Biopharmaceutical Statistics Pub Date : 2025-03-01 Epub Date: 2024-02-09 DOI:10.1080/10543406.2024.2311254
Yifan Zhou, Abigail Sloan, Sandeep Menon, Ling Wang
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Abstract

Combination therapies with multiple mechanisms of action can offer improved efficacy and/or safety profiles when compared to a single therapy with one mechanism of action. Consequently, the number of combination therapy studies have increased multi-fold, both in oncology and non-oncology indications. However, identifying the optimal doses of each drug in a combination therapy can require a large sample size and prolong study timelines, especially when full factorial designs are used. In this paper, we extend the MCP-Mod design of Bretz, Pinheiro, and Branson to a three-dimensional space to model the dose-response surface of a two-drug combination under the framework of Combination (Comb) MCP-Mod. The resulting model yields a set of dosages for each drug in the combination that elicits the target response so that an optimal dose for the combination can be selected for pivotal studies. We construct three-dimensional dose-response models for the combination and formulate the contrast test statistic to select the best model, which can then be used to select the optimal dose. Guidance to calculate power and sample size calculations are provided to assist study design. Simulation studies show that Comb MCP-Mod performs as well as the conventional multiple comparisons approach in controlling the family-wise error rate at the desired alpha level. However, Comb MCP-Mod is more powerful than the classical multiple comparisons approach in detecting dose-response relationships when treatment is non-null. The probability of correctly identifying the underlying dose-response relationship is generally higher when using Comb MCP-Mod than when using the multiple comparisons approach.

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用于双药联合剂量范围研究的 MCP-Mod 组合。
与只有一种作用机制的单一疗法相比,具有多种作用机制的联合疗法可以提高疗效和/或安全性。因此,无论是肿瘤还是非肿瘤适应症,联合疗法研究的数量都成倍增加。然而,确定联合疗法中每种药物的最佳剂量可能需要大量样本,并延长研究时间,尤其是采用全因子设计时。在本文中,我们将 Bretz、Pinheiro 和 Branson 的 MCP-Mod 设计扩展到三维空间,在组合(Comb)MCP-Mod 框架下建立双药组合的剂量-反应曲面模型。由此建立的模型可以为联合用药中的每种药物得出一组能引起目标反应的剂量,这样就可以为关键性研究选择联合用药的最佳剂量。我们为联合用药构建了三维剂量-反应模型,并制定了对比测试统计量,以选择最佳模型,然后可用于选择最佳剂量。我们还提供了功率计算和样本量计算指南,以帮助研究设计。模拟研究表明,Comb MCP-Mod 与传统的多重比较方法一样,能在所需的α水平上控制族内误差率。不过,在检测治疗为非空时的剂量-反应关系时,Comb MCP-Mod 比传统多重比较法更有效。与多重比较法相比,使用 Comb MCP-Mod 法正确识别潜在剂量-反应关系的概率通常更高。
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来源期刊
Journal of Biopharmaceutical Statistics
Journal of Biopharmaceutical Statistics 医学-统计学与概率论
CiteScore
2.50
自引率
18.20%
发文量
71
审稿时长
6-12 weeks
期刊介绍: The Journal of Biopharmaceutical Statistics, a rapid publication journal, discusses quality applications of statistics in biopharmaceutical research and development. Now publishing six times per year, it includes expositions of statistical methodology with immediate applicability to biopharmaceutical research in the form of full-length and short manuscripts, review articles, selected/invited conference papers, short articles, and letters to the editor. Addressing timely and provocative topics important to the biostatistical profession, the journal covers: Drug, device, and biological research and development; Drug screening and drug design; Assessment of pharmacological activity; Pharmaceutical formulation and scale-up; Preclinical safety assessment; Bioavailability, bioequivalence, and pharmacokinetics; Phase, I, II, and III clinical development including complex innovative designs; Premarket approval assessment of clinical safety; Postmarketing surveillance; Big data and artificial intelligence and applications.
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