纵向模型的视觉预测检查和掉队的预测检查.
Chuanpu Hu1, Anna G Kondic2, Amit Roy3
1Clinical Pharmacology, Pharmacometrics & Bioanalysis, Bristol Myers Squibb, 3551 Lawrenceville-Princeton Road, Lawrenceville, NJ, 08540, USA. chuanpu.hu@bms.com.
Journal of pharmacokinetics and pharmacodynamics
|August 18, 2024
概括
视觉预测检查 (VPCs) 可以通过在临床试验中考虑患者退出来改进. 使用基于观察数据的置信区间的有条件方法,可以比传统方法更有效地评估药理学模型.
科学领域:
- 药学指标 (Pharmacometrics) 是一个指标.
- 临床试验方法论 临床试验方法论
- 统计建模 统计建模
背景情况:
- 视觉预测检查 (VPC) 是评估药量模型的标准.
- 在临床试验 (尤其是瘤学) 中常见的患者退出,可能会影响VPC的性能.
- 对于VPCs,现有的脱落处理方法缺乏对其差异和适用性的清晰理解.
研究的目的:
- 澄清处理VPC中脱落的方法及其适当的使用.
- 引入一种改进的VPC方法,使用从观察到的数据构建的置信区间.
- 为了比较不同VPC方法与dropout的性能.
主要方法:
- 开发了一个理论框架,用于将退出纳入VPC.
- 提出并实施了两种方法:完整的 (参数时间到事件) 和条件的 (参数或考克斯比例危险模型).
- 应用方法用于瘤生长动态 (TGD) 建模,使用来自两项癌症试验 (尼沃卢马布,多塞塔塞尔) 的数据,其中包括来自855名受试者的3504项测量.
主要成果:
- 完整的方法在TGD模型评估中显示出与天真VPC相比的有限改善 (没有脱落调整).
- 使用韦布尔或考克斯比例危险模型的有条件方法优于全方位方法.
- 置信区间增强了VPC的解释;条件方法更普遍地适用于学.
结论:
- 条件VPC方法,结合从观察到的数据的置信区间,提供一个更强大的评估,当患者学发生.
- 这种方法对于复杂的模型特别有价值,例如瘤学试验中的TGD.
- 非参数方法可以在VPC分析中提供额外的稳定性.
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