Basu Anirban
Departments of Health Services and Pharmacy, University of Washington, Seattle, 1959 NE Pacific St, Box 357660, Seattle, WA 98195-7660, USA.
Stat Biosci. 2011 Sep;3(1):6-27. doi: 10.1007/s12561-011-9033-6.
Instrumental variables methods (IV) are widely used in the health economics literature to adjust for hidden selection biases in observational studies when estimating treatment effects. Less attention has been paid in the applied literature to the proper use of IVs if treatment effects are heterogeneous across subjects. Such a heterogeneity in effects becomes an issue for IV estimators when individuals' self-selected choices of treatments are correlated with expected idiosyncratic gains or losses from treatments. We present an overview of the challenges that arise with IV estimators in the presence of effect heterogeneity and self-selection and compare conventional IV analysis with alternative approaches that use IVs to directly address these challenges. Using a Medicare sample of clinically localized breast cancer patients, we study the impact of breast-conserving surgery and radiation with mastectomy on 3-year survival rates. Our results reveal the traditional IV results may have masked important heterogeneity in treatment effects. In the context of these results, we discuss the advantages and limitations of conventional and alternative IV methods in estimating mean treatment-effect parameters, the role of heterogeneity in comparative effectiveness research and the implications for diffusion of technology.
工具变量法（IV）在卫生经济学文献中被广泛用于在估计治疗效果时调整观察性研究中隐藏的选择偏差。在应用文献中，如果治疗效果在不同受试者之间存在异质性，那么对于工具变量的正确使用关注较少。当个体对治疗的自我选择与治疗预期的特异收益或损失相关时，这种效果异质性就成为工具变量估计量的一个问题。我们概述了在存在效果异质性和自我选择的情况下工具变量估计量所面临的挑战，并将传统的工具变量分析与使用工具变量直接应对这些挑战的替代方法进行比较。利用医疗保险中临床局限性乳腺癌患者的样本，我们研究了保乳手术加放疗与乳房切除术对3年生存率的影响。我们的结果表明，传统的工具变量结果可能掩盖了治疗效果中重要的异质性。结合这些结果，我们讨论了传统和替代工具变量方法在估计平均治疗效果参数方面的优缺点、异质性在比较效果研究中的作用以及对技术传播的影响。
