观察性研究中的倾向性评分方法的性能:系统性审查
Mahin Tatari1,2, Stefano Rosato3, Paola D'Errigo3
1Statistical Science Department, Sapienza University of Rome, Rome, Italy. mahin.tatari@uniroma1.it.
倾向性得分匹配 (PSM) 和治疗权重的逆概率 (IPTW) 有效地减少偏差并改善观察性研究中的共变量平衡. 使用IPTW的两倍强大的方法提供可靠的推断,并最大限度地降低平均平方误差 (MSE).
科学领域:
- 流行病学和生物统计学
- 卫生研究方法论 卫生研究方法论
- 统计推理 统计推理
背景情况:
- 观察性研究在健康研究中至关重要,但估计治疗效果需要强大的方法.
- 关于倾向分数 (PS) 方法的现有研究是分散的,需要进行全面的综合.
- 对于研究人员选择合适的PS方法的指导是至关重要的.
研究的目的:
- 系统地审查和综合各种倾向评分方法的性能证据.
- 为在观察性研究中使用PS方法的研究人员提供实际建议.
- 确定最有效的PS技术来估计治疗效果.
主要方法:
- 一个叙事综合系统性审查,遵循PRISMA指南.
- 包括2000年至2025年间发表的同行评审研究.
- 基于治疗效果估计,偏差,平均平方误差 (MSE) 和置信区间 (CI) 覆盖范围的研究评估.
主要成果:
- 倾向性得分匹配 (PSM) 和治疗权重的逆概率 (IPTW) 通常会提高共变量平衡和CI覆盖率.
- 基于校准器的最佳和完全匹配有效地减少了赔率比率 (OR) 和边际危险比率 (MHR) 的偏差.
- 与IPTW相结合的双重稳定 (DR) 方法显示出低MSE和可靠的推断,而PS分层通常显示出较差的性能.
结论:
- 倾向性得分匹配 (PSM),加倍强大的治疗权重反向概率 (DR-IPTW) 和完全匹配非常有效.
- 这些方法在减少偏差,实现共变量平衡,最小化MSE和提高精度方面表现出色.
- 这些发现为在观察性研究中选择最佳PS方法提供了实际指导.
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