Division of Oral Epidemiology and Dental Public Health, Department of Preventive and Restorative Dental Sciences, University of California at San Francisco, San Francisco, California.
Department of Statistics and Data Science, Yale University, New Haven, Connecticut.
Am J Epidemiol. 2018 Mar 1;187(3):614-622. doi: 10.1093/aje/kwx282.
To address issues with measured and unmeasured confounding in observational studies, we developed a unified approach to using an instrumental variable in more flexible ways to evaluate treatment effects. The approach is based on an instrumental propensity score conditional on baseline variables, which can then be incorporated in matching, regression, subclassification, or weighting along with various parametric, semiparametric, or nonparametric methods for the assessment of treatment effects. Therefore, the application of the instrumental propensity score allows different methods for outcome effect evaluations in addition to standard 2-stage least square models while controlling for unmeasured confounders. Several properties of the instrumental propensity score are discussed. The approach is then illustrated using subclassification along with a semiparametric density ratio model and empirical likelihood. This method allows us to evaluate distributional and subgroup treatment effects in addition to the overall average treatment effect. Simulation studies showed that the method works well. We applied our method to a study of the effects of attending a Catholic school versus a public school and found that attending a Catholic school had significant beneficial effects on subsequent wages among a subgroup of subjects.
为了解决观察性研究中测量和未测量混杂的问题，我们开发了一种统一的方法，通过更灵活地使用工具变量来评估治疗效果。该方法基于基于基线变量的工具倾向评分，然后可以将其与各种参数、半参数或非参数方法一起用于匹配、回归、分类或加权，以评估治疗效果。因此，工具倾向评分的应用允许在控制未测量混杂因素的情况下，除了标准的两阶段最小二乘法模型之外，还可以使用不同的方法来评估结果效应。讨论了工具倾向评分的几个属性。然后，我们使用半参数密度比模型和经验似然进行分类来说明该方法。这种方法允许我们评估分布和亚组治疗效果，以及总体平均治疗效果。模拟研究表明该方法效果良好。我们将我们的方法应用于一项研究，该研究比较了上天主教学校和上公立学校的效果，结果发现，对于一部分学生，上天主教学校对上大学后的工资有显著的积极影响。
