In observational studies, subjects are often nested within clusters. In medical studies, patients are often treated by doctors and therefore patients are regarded as nested or clustered within doctors. A concern that arises with clustered data is that cluster-level characteristics (e.g., characteristics of the doctor) are associated with both treatment selection and patient outcomes, resulting in cluster-level confounding. Measuring and modeling cluster attributes can be difficult and statistical methods exist to control for all unmeasured cluster characteristics. An assumption of these methods however is that characteristics of the cluster and the effects of those characteristics on the outcome (as well as probability of treatment assignment when using covariate balancing methods) are constant over time. In this paper, we consider methods that relax this assumption and allow for estimation of treatment effects in the presence of unmeasured time-dependent cluster confounding. The methods are based on matching with the propensity score and incorporate unmeasured time-specific cluster effects by performing matching within clusters or using fixed- or random-cluster effects in the propensity score model. The methods are illustrated using data to compare the effectiveness of two total hip devices with respect to survival of the device and a simulation study is performed that compares the proposed methods. One method that was found to perform well is matching within surgeon clusters partitioned by time. Considerations in implementing the proposed methods are discussed.

中文翻译：

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时间相关聚类混杂的倾向评分方法

在观察性研究中，受试者通常嵌套在集群中。在医学研究中，患者通常由医生治疗，因此患者被视为嵌套或聚集在医生体内。集群数据引起的一个问题是集群级别的特征（例如，医生的特征）与治疗选择和患者结果相关，从而导致集群级别的混杂。测量和建模集群属性可能很困难，并且存在统计方法来控制所有未测量的集群特征。然而，这些方法的假设是集群的特征和这些特征对结果的影响（以及使用协变量平衡方法时的治疗分配概率）随着时间的推移是恒定的。在本文中，我们考虑放宽这一假设的方法，并允许在存在未测量的时间相关集群混杂的情况下估计治疗效果。这些方法基于与倾向得分的匹配，并通过在集群内执行匹配或在倾向得分模型中使用固定或随机集群效应来合并未测量的特定时间集群效应。使用数据来说明这些方法，以比较两个全髋关节装置在装置存活方面的有效性，并进行模拟研究以比较所提出的方法。一种被发现表现良好的方法是在按时间划分的外科医生集群内进行匹配。讨论了实施建议方法的注意事项。