频率主义者和贝叶斯的耐受性间隔,为左边审查的马分布的药物质量属性设定规范限制
1Alnylam Pharmaceuticals, Inc, Cambridge, Massachusetts, USA.
Pharmaceutical statistics
|October 23, 2023
概括
使用非信息化先验的贝叶斯方法优于最大概率估计,用于用审查数据设置药物产品规格. 这种方法提供了实际上有用的宽容区间,特别是在处理左边审查的马分布数据时.
科学领域:
- 制药科学 制药科学
- 统计建模 统计建模
- 质量控制 质量控制 质量控制
背景情况:
- 建立药物产品规范限制依赖于从质量属性测量中获得的耐受性间隔.
- 在药品质量控制中,测量低于报告限值的左边审查数据是常见的.
- 马分布通常适用于在属性测量中遇到的右倾数据.
研究的目的:
- 为了比较最大概率估计 (MLE) 和贝叶斯方法来估计被审查的马分布的参数.
- 评估这些方法在不同样本大小和审查级别下计算公差间隔时的性能.
- 在贝叶斯估计中评估不同先前选择 (非信息参考先前和最大数据信息先前) 的影响.
主要方法:
- 模拟研究比较MLE和贝叶斯的方法进行审查的马分布参数估计.
- 使用偏差,根平均平方误差,平均长度和置信系数来评估公差间隔计算.
- 在贝叶斯分析中应用非信息化参考先验和最大数据信息先验 (MDIP).
主要成果:
- 在评估的情景中,使用非信息参考先验的贝叶斯方法在参数估计和容忍区间计算方面普遍超过了MLE.
- 增加样本大小改善了所有测试方法,而增加审查的程度则恶化了它们的性能.
- 使用MDIP的贝叶斯方法对小样本尺寸产生了过于宽的容忍间隔,使它们不适合规范设置.
结论:
- 使用非信息先验的贝叶斯方法为药物产品规范设置提供了实际上有用的耐受性极限,尽管计算强度高,但其表现优于MLE.
- 虽然MLE更简单,但在产生可靠的耐受性限值方面可能是不准确和不精确的,特别是在具有挑战性的数据条件下.
- 前期和样本大小的选择显著影响了在药品质量控制中对审查数据分析的贝叶斯方法的性能.
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