Franklin Jessica M, Schneeweiss Sebastian, Huybrechts Krista F, Glynn Robert J
From the Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA.
Epidemiology. 2015 Mar;26(2):238-41. doi: 10.1097/EDE.0000000000000241.
In nonrandomized studies of comparative effectiveness of medications, the prescriber may be the most important determinant of treatment assignment, yet the majority of analyses ignore the prescriber. Via Monte Carlo simulation, we evaluated the bias of 3 approaches that utilize the prescriber in analysis compared against the default approach that ignores the prescriber. Prescriber preference instrumental variable (IV) analyses were unbiased when IV criteria were met, which required no clustering of unmeasured patient characteristics within prescriber. In all other scenarios, IV analyses were highly biased, and stratification on the prescriber reduced confounding bias at the patient or prescriber levels. Including a prescriber random intercept in the propensity score model reversed the direction of confounding from measured patient factors and resulted in unpredictable changes in bias. Therefore, we recommend caution when using the IV approach, particularly when the instrument is weak. Stratification on the prescriber may be more robust; this approach warrants additional research.
在药物疗效的非随机对照研究中，开处方者可能是治疗分配的最重要决定因素，但大多数分析都忽略了开处方者。通过蒙特卡洛模拟，我们评估了三种在分析中利用开处方者的方法与忽略开处方者的默认方法相比的偏差。当满足工具变量（IV）标准时，开处方者偏好工具变量分析是无偏的，这要求在开处方者内部未测量的患者特征不存在聚类。在所有其他情况下，IV分析存在高度偏差，并且按开处方者进行分层可降低患者或开处方者层面的混杂偏差。在倾向得分模型中纳入开处方者随机截距会逆转已测量患者因素的混杂方向，并导致偏差出现不可预测的变化。因此，我们建议在使用IV方法时要谨慎，尤其是当工具变量较弱时。按开处方者进行分层可能更稳健；这种方法值得进一步研究。
