Efficient Study Design and Analysis of Longitudinal Dose-Response Data Using Fractional Polynomials.

IF 1.3 4区 医学 Q4 PHARMACOLOGY & PHARMACY Pharmaceutical Statistics Pub Date : 2024-07-28 DOI:10.1002/pst.2425
Benjamin F Hartley, Dave Lunn, Adrian P Mander
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Abstract

Correctly characterising the dose-response relationship and taking the correct dose forward for further study is a critical part of the drug development process. We use optimal design theory to compare different designs and show that using longitudinal data from all available timepoints in a continuous-time dose-response model can substantially increase the efficiency of estimation of the dose-response compared to a single timepoint model. We give theoretical results to calculate the efficiency gains for a large class of these models. For example, a linearly growing Emax dose-response in a population with a between/within-patient variance ratio ranging from 0.1 to 1 measured at six visits can be estimated with between 1.43 and 2.22 times relative efficiency gain, or equivalently, with 30% to a 55% reduced sample size, compared to a single model of the final timepoint. Fractional polynomials are a flexible way to incorporate data from repeated measurements, increasing precision without imposing strong constraints. Longitudinal dose-response models using two fractional polynomial terms are robust to mis-specification of the true longitudinal process while maintaining, often large, efficiency gains. These models have applications for characterising the dose-response at interim or final analyses.

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利用分数多项式进行高效研究设计和纵向剂量反应数据分析
正确描述剂量-反应关系并采取正确的剂量进行进一步研究是药物开发过程的关键部分。我们利用最优设计理论来比较不同的设计,结果表明,与单时间点模型相比,在连续时间剂量-反应模型中使用所有可用时间点的纵向数据可以大大提高剂量-反应的估算效率。我们给出了计算一大类此类模型效率提高的理论结果。例如,与最后一个时间点的单一模型相比,在一个患者间/患者内变异比在 0.1 到 1 之间的人群中,通过六次就诊测量的线性增长的 Emax 剂量反应的估计效率相对提高了 1.43 到 2.22 倍,或者说样本量减少了 30% 到 55%。分数多项式是纳入重复测量数据的一种灵活方法,可在不强加限制的情况下提高精确度。使用两个分数多项式项的纵向剂量-反应模型对真实纵向过程的错误规范具有很强的鲁棒性,同时还能保持较高的效率。这些模型可用于描述中期或最终分析的剂量反应特征。
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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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