Joffe Marshall M, Yang Wei Peter, Feldman Harold I
University of Pennsylvania School of Medicine, PA, USA.
Int J Biostat. 2010;6(2):Article 11. doi: 10.2202/1557-4679.1199.
Most attempts at causal inference in observational studies are based on assumptions that treatment assignment is ignorable. Such assumptions are usually made casually, largely because they justify the use of available statistical methods and not because they are truly believed. It will often be the case that it is plausible that conditional independence holds at least approximately for a subset but not all of the experience giving rise to one's data. Such selective ignorability assumptions may be used to derive valid causal inferences in conjunction with structural nested models. In this paper, we outline selective ignorability assumptions mathematically and sketch how they may be used along with otherwise standard G-estimation or likelihood-based methods to obtain inference on structural nested models. We also consider use of these assumptions in the presence of selective measurement error or missing data when the missingness is not at random. We motivate and illustrate our development by considering an analysis of an observational database to estimate the effect of erythropoietin use on mortality among hemodialysis patients.
在观察性研究中，大多数因果推断尝试都基于治疗分配可忽略这一假设。此类假设通常是随意做出的，很大程度上是因为它们为使用现有的统计方法提供了正当理由，而非因为人们真的相信它们。通常会出现这样的情况：对于产生某人数据的部分而非全部经验而言，条件独立性至少近似成立是合理的。这种选择性可忽略性假设可与结构嵌套模型一起用于得出有效的因果推断。在本文中，我们从数学上概述了选择性可忽略性假设，并简述了如何将它们与其他标准的G估计或基于似然的方法一起使用，以获得关于结构嵌套模型的推断。我们还考虑了在存在选择性测量误差或缺失数据（缺失并非随机）的情况下使用这些假设。我们通过对一个观察性数据库进行分析来估计使用促红细胞生成素对血液透析患者死亡率的影响，以此推动并阐明我们的研究进展。
