在使用倾向性得分匹配和权重与时间到事件结果时,对风险差异的方差估计
Guy Cafri1, Peter C Austin2,3,4
1Medical Device Epidemiology and Real-World Data Sciences, J&J Medical Devices and Office of the Chief Medical Officer, New Jersey, USA.
Pharmaceutical statistics
|May 31, 2023
概括
本研究将倾向性得分方法进行比较,以估计在时间到事件数据中的绝对治疗效应. 在观察性研究中,建议使用权重和匹配方法来准确地估计点和方差.
科学领域:
- 流行病学 流行病学
- 生物统计学 生物统计学
- 健康 结果 研究 研究 结果
背景情况:
- 观察性研究对于估计治疗效果至关重要,但容易引起混.
- 倾向得分方法通常用于调整这些研究中的混.
- 时间到事件的结果是常见的,相对和绝对效应指标都很重要.
研究的目的:
- 描述估计风险差异 (绝对效应) 的方法,使用时间到事件数据的倾向分数.
- 通过蒙特卡洛模拟来比较不同倾向得分方法的性能.
- 为选择适当的方法提供指导,用于点和方差估计.
主要方法:
- 倾向性得分权重 (IPTW,ATT权重) 和匹配方法的描述.
- 蒙特卡洛模拟用于评估偏差,差异精度和置信区间覆盖范围.
- 将方法应用于真实世界数据集,用于心肌梗塞后的他类药物处方.
主要成果:
- 权衡方法 (未修剪的ATT重量,IPTW) 和标尺匹配显示出对点估计的良好表现,特别是在治疗流行率低的情况下.
- 支持加权强标准错误,引导方法和与格林伍德方法匹配用于标准错误估计.
- 模拟结果提供了各种倾向评分技术相对性能的见解.
结论:
- 倾向性评分方法,特别是权重和校准匹配,对于在时间到事件分析中估计绝对处理效应是有价值的.
- 要获得可靠的结果,必须仔细考虑点和方差估计的方法.
- 这些发现有助于研究人员在观察性研究中选择适当的分析策略.
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