开发和比较模型综合证据方法,用于与药物动力学终点的生物等价性研究
Xiaomei Chen1, Henrik B Nyberg1, Mark Donnelly2
1Department of Pharmacy, Uppsala University, Uppsala, Sweden.
使用非线性混合效应模型进行模型综合证据 (MIE) 分析,改善了稀疏的药理动力学数据的生物等价性评估. 采样的重要性重新采样 (SIR) 控制I型错误,提供比非分区分析更高的功率.
科学领域:
- 药理动力学 药理动力学
- 统计建模 统计建模
- 生物等价性研究 生物等价性研究
背景情况:
- 非线性混合效应 (NLME) 模型能够通过稀疏采样对生物等价性 (BE) 数据进行模型综合证据 (MIE) 分析,克服非分区分析 (NCA) 的局限性.
- 现有的MIE方法可能会因低估的参数不确定性和非对称的正常性假设而增加I型错误.
研究的目的:
- 开发和评估一个改进的MIE生物等价性分析方法.
- 解决以前的MIE方法的局限性,特别是关于参数不确定性和I型错误控制.
主要方法:
- 开发了一种新的MIE生物等价性分析方法,涉及模型拟合,不确定性评估,模拟和BE确定.
- 仅将治疗,序列和周期影响纳入吸收参数.
- 使用模拟步骤来生成置信区间.
- 探索非参数引导和采样重要性重新采样 (SIR) 用于参数不确定性评估.
主要成果:
- 使用SIR对参数不确定性定量化的MIE方法有效控制了0.05名义水平的I型错误.
- 这种控制甚至在小样本大小和稀疏的药理动力学数据下也保持了.
- 与NCA相比,开发的MIE方法表现出更高的统计能力,特别是使用较少和更可变的数据.
结论:
- 新的MIE方法,特别是SIR,提供了一个可靠的方法,用于生物等价性评估稀疏的药理动力学数据.
- 这种方法提供了改进的I型错误控制和比传统的NCA方法更强大的功率.
- 这些发现支持使用先进的MIE技术进行更可靠的生物等价性评估.
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