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枢机性匹配与倾向性得分匹配用于解决可植入医疗器械和外科流行病学中的集群级残留混问题:一个参数和等离子模拟研究

Mike Du¹, Stephen Johnston², Paul M Coplan²

¹Pharmaco- and Device Epidemiology Group, Health Data Sciences, Botnar Research Centre, NDORMS, University of Oxford, Windmill Road, Oxford, OX3 7LD, UK.

BMC medical research methodology

|November 23, 2024

概括

枢密度匹配 (CM) 保留了更多的受试者,但比倾向性得分匹配 (PSM) 引入了更多的偏差,用于植入式设备的市场后监测. 为了减少观察性研究中的混偏差,PSM是首选的.

科学领域:

医疗器械流行病学医疗器械流行病学
观察性研究方法的方法.
医疗服务研究医疗服务研究

背景情况:

由于快速的创新和新的法规,可植入器械的营销后监测至关重要.
在评估风险和益处时,观察性研究面临多层次混 (患者,外科医生,医院) 的挑战.
将匹配方法进行比较对于在复杂的医疗数据中强有力的偏差减少至关重要.

研究的目的:

为了比较枢密度匹配 (CM) 与倾向得分匹配 (PSM) 在减少混偏差方面的有效性.
在模拟中评估CM和PSM在存在集群级混时的性能.
评估每个匹配方法的样本保留和偏差减少之间的权衡.

主要方法:

进行了参数和等离子模式蒙特卡洛模拟,患者对的比例和样本大小各不相同.
使用0.1标准化平均差异 (SMD) 门和PSM进行集群内部和跨集群平衡的应用CM.
使用对匹配样本的逻辑回归来估计治疗效应,以量化偏差和保留.

主要成果:

与倾向性得分匹配 (PSM) 相比,基本性匹配 (CM) 在大多数场景中显示出更高的样本保留率.
PSM的相对偏差总是低于CM,特别是在跨集群匹配场景中.
等离子模拟证实了参数模拟的发现,表明了类似的性能趋势.

结论:

在复杂的混场景中,CM提供了更好的样本保留,但比PSM引入了更多的偏差.
在集群级别的混因素的背景下,PSM是一种更有效的方法来减少混偏差.
需要进一步的研究来优化CM,特别是在医疗器械和外科流行病学中混约束设置方面.

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