Selective and Future Ignorability in Causal Inference

描述(由申请人提供)：在观察性研究中，大多数因果推断的尝试都是基于治疗分配可以忽略的假设。这样的假设通常是随意做出的，而且往往是不可信的，部分原因是没有关于混杂因素的足够信息。在最近的工作中，我们对可忽略假设的变体进行了形式化，我们称之为选择性和未来可忽略假设，它可以更准确地表示许多研究中获得的情况。在选择性可忽略的情况下，条件独立性在已知的数据子集中获得；在未来可忽略的情况下，治疗和潜在结果的独立性取决于测量的协变量历史和未来潜在结果的组合。我们已经开发了初步的推理方法，当选择性和/或未来的可知性条件获得而标准可知性条件不获得时，这些方法比标准方法更合适。在这个项目中，我们将扩展我们在选择性和未来可忽略假设方面的工作。为此，我们将1)扩展我们在制定这些假设方面的工作，特别是在考虑估计治疗对重复测量和失败时间结果的组合的影响方面；2)研究在这些假设下使G估计成为实用估计选项的方法；3)开发和研究选择性和未来可知性下G估计的替代方法；为此，我们将考虑最大似然法、目标最大似然法和贝叶斯方法；4)当感兴趣的治疗被错误测量时，使用选择性可忽略性假设来开发更可靠的推断方法；5)使用来自美国肾脏数据系统的数据，使用开发的方法评估促红细胞生成素对接受慢性血液透析的终末期肾病患者的红细胞压积水平和死亡率的影响。这是一个最近引起人们极大兴趣的话题。因此，该项目将开发有用的推理方法，以便在更合理的假设下使用，并将这些方法应用于一个重要的实际问题。 公共卫生相关性：该项目将开发和评估在纵向研究中控制混杂的新假设，以及在这些假设下估计治疗效果的新方法。该项目将使用来自美国肾脏数据系统的数据，使用这些方法来估计促红细胞生成素对红细胞压积和死亡率的影响。

DESCRIPTION (provided by applicant): Most attempts at causal inference in observational studies are based on assumptions that treatment assignment is ignorable. Such assumptions are usually made casually and are often implausible, in part because adequate information on confounders is not available. In recent work, we have formalized variants of ignorability assumptions, which we term selective and future ignorability, which can more correctly represent the situation obtaining in many studies. Under selective ignorability, conditional independence obtains in a known subset of the data; under future ignorability, independence of treatment and potential outcomes holds conditionally on a combination of measured covariate history and future potential outcomes. We have developed initial approaches to inference which are more appropriate than standard methods when selective and/or future ignorability conditions obtain but standard ignorability conditions do not. In this project, we will extend our work on selective and future ignorability assumptions. To this end, we will 1) Extend our work in formulating these assumptions, in particular in considering estimating the effects of a treatment on a combination of repeated measures and failure-time outcomes; 2) Investigate ways to make G-estimation a practical estimation option under these assumptions; 3) Develop and investigate alternatives to G-estimation under selective and future ignorability; for this, we will consider maximum likelihood, targeted maximum likelihood, and Bayesian approaches; 4) Use selective ignorability assumptions to develop more reliable methods of inference when the treatment of interest is mismeasured; and 5) Use the methods developed to estimate the effect of erythropoietin use on hematocrit levels and mortality among subjects with end-stage renal disease receiving chronic hemodialysis, using data from the United States Renal Data System. This is a topic that has recently generated substantial interest. Thus, the project will develop useful inferential methods for use under more reasonable assumptions, and will apply those methods to an important practical problem. PUBLIC HEALTH RELEVANCE: The project will develop and evaluate new assumptions for controlling confounding in longitudinal studies and new methods for estimating treatment effects under those assumptions. The project will use those methods in estimating the effect of erythropoietin on hematocrit and on mortality using data from the United States Renal Data System.