纵向模型和辍学的可视化预测检查。

IF 2.2 4区 医学 Q3 PHARMACOLOGY & PHARMACY Journal of Pharmacokinetics and Pharmacodynamics Pub Date : 2024-12-01 Epub Date: 2024-08-18 DOI:10.1007/s10928-024-09937-4
Chuanpu Hu, Anna G Kondic, Amit Roy
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引用次数: 0

摘要

目测预测检查(VPC)通常用于评估药物计量学模型。然而,如果预后较差的患者较早退出临床试验(尤其是肿瘤临床试验),则这些模型的性能可能会受到影响。虽然文献中已经出现了考虑辍学的方法,但这些方法在假设、灵活性和性能方面各不相同,而且它们之间的差异尚未得到广泛了解。本稿件旨在阐明哪些方法可用于处理有遗漏的 VPC,以及何时处理,同时提出一种使用置信区间的信息量更大的 VPC 方法。此外,我们还建议根据观测数据而不是模拟数据来构建置信区间。我们建立了将辍学纳入 VPC 的理论框架,并将其应用于提出两种方法:完全方法和条件方法。完全方法是通过参数时间到事件模型实现的,而条件方法是通过参数模型和考克斯比例危险(CPH)模型实现的。这些方法的实际性能通过应用于肿瘤生长动态(TGD)建模来说明,该模型的数据来自两项癌症临床试验,分别为尼伐单抗(nivolumab)和多西他赛(docetaxel),对患者进行随访直至疾病进展。数据集包括来自 855 名受试者的 3504 次肿瘤大小测量数据,这些数据由 TGD 模型描述。受试者的辍学情况由 Weibull 或 CPH 模型描述。为了进一步说明 VPC 方法的特性,还使用了模拟数据集。结果表明,与不调整辍学的天真方法相比,人们更熟悉的完全方法可能无法为 TGD 模型评估提供有意义的改进,而使用 Weibull 模型或 Cox 比例危险模型的条件方法可能会更胜一筹。总的来说,在 VPC 中加入置信区间应能改善解释,条件方法在发生辍学时更普遍适用,而非参数方法可以提供额外的稳健性。
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Visual predictive check of longitudinal models and dropout.

Visual predictive checks (VPC) are commonly used to evaluate pharmacometrics models. However their performance may be hampered if patients with worse outcomes drop out earlier, as often occurs in clinical trials, especially in oncology. While methods accounting for dropouts have appeared in literature, they vary in assumptions, flexibility, and performance, and the differences between them are not widely understood. This manuscript aims to elucidate which methods can be used to handle VPC with dropout and when, along with a more informative VPC approach using confidence intervals. Additionally, we propose constructing the confidence interval based on the observed data instead of the simulated data. The theoretical framework for incorporating dropout in VPCs is developed and applied to propose two approaches: full and conditional. The full approach is implemented using a parametric time-to-event model, while the conditional approach is implemented using both parametric and Cox proportional-hazard (CPH) models. The practical performances of these approaches are illustrated with an application to the tumor growth dynamics (TGD) modeling of data from two cancer clinical trials of nivolumab and docetaxel, where patients were followed until disease progression. The dataset consisted of 3504 tumor size measurements from 855 subjects, which were described by a TGD model. The dropout of subjects was described by a Weibull or CPH model. Simulated datasets were also used to further illustrate the properties of the VPC methods. The results showed that the more familiar full approach might not provide meaningful improvement for TGD model evaluation over the naive approach of not adjusting for dropout, and could be outperformed by the conditional approach using either the Weibull model or the Cox proportional hazard model. Overall, including confidence intervals in VPC should improve interpretation, the conditional approach was shown to be more generally applicable when dropout occurs, and the nonparametric approach could provide additional robustness.

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来源期刊
CiteScore
4.90
自引率
4.00%
发文量
39
审稿时长
6-12 weeks
期刊介绍: Broadly speaking, the Journal of Pharmacokinetics and Pharmacodynamics covers the area of pharmacometrics. The journal is devoted to illustrating the importance of pharmacokinetics, pharmacodynamics, and pharmacometrics in drug development, clinical care, and the understanding of drug action. The journal publishes on a variety of topics related to pharmacometrics, including, but not limited to, clinical, experimental, and theoretical papers examining the kinetics of drug disposition and effects of drug action in humans, animals, in vitro, or in silico; modeling and simulation methodology, including optimal design; precision medicine; systems pharmacology; and mathematical pharmacology (including computational biology, bioengineering, and biophysics related to pharmacology, pharmacokinetics, orpharmacodynamics). Clinical papers that include population pharmacokinetic-pharmacodynamic relationships are welcome. The journal actively invites and promotes up-and-coming areas of pharmacometric research, such as real-world evidence, quality of life analyses, and artificial intelligence. The Journal of Pharmacokinetics and Pharmacodynamics is an official journal of the International Society of Pharmacometrics.
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