Shen Changyu, Li Xiaochun, Li Lingling, Were Martin C
Division of Biostatistics, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN 46202, USA.
Biom J. 2011 Sep;53(5):822-37. doi: 10.1002/bimj.201100042. Epub 2011 Jul 19.
Evaluation of impact of potential uncontrolled confounding is an important component for causal inference based on observational studies. In this article, we introduce a general framework of sensitivity analysis that is based on inverse probability weighting. We propose a general methodology that allows both non-parametric and parametric analyses, which are driven by two parameters that govern the magnitude of the variation of the multiplicative errors of the propensity score and their correlations with the potential outcomes. We also introduce a specific parametric model that offers a mechanistic view on how the uncontrolled confounding may bias the inference through these parameters. Our method can be readily applied to both binary and continuous outcomes and depends on the covariates only through the propensity score that can be estimated by any parametric or non-parametric method. We illustrate our method with two medical data sets.
评估潜在未控制混杂因素的影响是基于观察性研究进行因果推断的重要组成部分。在本文中，我们介绍了一种基于逆概率加权的敏感性分析通用框架。我们提出了一种通用方法，该方法允许进行非参数和参数分析，这两种分析由两个参数驱动，这两个参数控制倾向得分的乘法误差的变化幅度及其与潜在结果的相关性。我们还引入了一个特定的参数模型，该模型提供了一个机制性观点，说明未控制的混杂因素如何通过这些参数使推断产生偏差。我们的方法可以很容易地应用于二元和连续结果，并且仅通过倾向得分依赖于协变量，而倾向得分可以通过任何参数或非参数方法进行估计。我们用两个医学数据集说明了我们的方法。
