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使用定量偏差分析来评估单臂试验与真实世界的数据外部控制

Christen Gray^1,2,3, Eleanor Ralphs², Matthew P Fox⁴

¹Real World Data Science, Biopharmaceuticals Medical Evidence, AstraZeneca, Cambridge, UK.

Pharmacoepidemiology and drug safety

|April 29, 2024

概括

定量偏差分析 (QBA) 有助于在单臂试验 (SAT) 中使用现实世界数据 (RWD) 作为外部控制时评估偏差. 这种方法为评估证据质量提供了一个框架,即使存在不可避免的偏见.

科学领域:

临床试验临床试验
医疗信息学医疗信息学
生物统计学生物统计学

背景情况:

现实世界数据 (RWD) 越来越多地被用作单臂试验 (SAT) 中的外部控制,用于监管提交.
在RWD和SAT之间数据生成的差异可以引入显著的偏差.
定量偏差分析 (QBA) 提供了一种方法来应对这些挑战.

研究的目的:

为了说明量化偏差分析 (QBA) 的应用,用于评估现实世界数据 (RWD) 外部控制中的偏差.
展示QBA如何在具有分子亚型的高级非小细胞肺癌 (NSCLC) 的情况下使用.
评估特定偏差对生存分析的影响.

主要方法:

描述了瘤学偏差的来源,将RWD与SAT进行比较.
模拟了一个数据集,用于使用假设的免疫治疗剂进行先进的NSCLC存活分析.
描述了三种偏差的影响:缺少混因子,暴露错误分类和结果评估.

主要成果:

对于每个模拟场景,使用常规分析估计了危险比率 (HRs).
通过选择适当的偏差模型和因素来估计偏差调整的治疗效应和不确定性.
观察到中等程度的偏差,在假设情景中具有可变的方向.

结论:

QBA为偏见分析提供了一个直观的框架,使证据能够进行批判性评估.
QBA的准确性取决于对偏差模型和因素的正确规范.
虽然研究设计应该尽量减少偏见,但QBA对于评估不可避免的偏见的影响和评估证据质量至关重要.

关键词:

偏见因素是导致偏见的因素.外部控制器的外部控制定量偏差分析分析量化偏差分析现实世界的数据数据.监管决策的制定方式

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