Principal stratification in causal inference
Principal stratification in causal inference
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DOI：
10.1111/j.0006-341x.2002.00021.x
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发表时间：
2002-03-01
期刊：
影响因子：
1.9
通讯作者：
Rubin, DB
中科院分区：
文献类型：
--
作者：
Frangakis, CE;Rubin, DB
许多科学问题要求对治疗比较进行治疗后变量的调整，但标准方法的被估量不是因果效应。为了解决这一不足，我们提出了一个总体框架，比较治疗调整后的变量，产生主要影响的基础上，主要分层。关于治疗后变量的主要分层是通过比较的每种治疗下该治疗后变量的联合电位值定义的受试者交叉分类。主效应是主层中的因果效应。主要分层的关键属性是它们不受治疗分配的影响，因此可以用作任何治疗前协变量，如年龄类别。因此，我们的主效应的中心属性是它们总是因果效应，并且不会受到标准治疗后调整被估量的并发症的影响。我们简要讨论了这种主要的因果效应是最近三种应用与治疗后变量调整之间的联系：（i）治疗不依从性，（ii）治疗不依从性后的缺失结局（脱落），（iii）死亡删失。然后，我们攻击的问题，替代或生物标志物的终点，我们表明，使用主要的因果效应，目前所有的代孕的定义，即使是完全正确的，一般不具有预期的解释为因果效应的治疗结果。我们继续制定estimands的基础上，主要分层和主因果效应，并显示其优越性。
Many scientific problems require that treatment comparisons be adjusted for posttreatment variables, butZ the estimands underlying standard methods are not causal effects. To address this deficiency, we propose a general framework for comparing treatments adjusting for posttreatment variables that yields principal effects based on principal stratification. Principal stratification with respect to a post treatment variable is a cross-classification of subjects defined by the joint potential values of that posttreatment variable under each of the treatments being compared. Principal effects are causal effects within a principal stratum. The key property of principal strata is that they are not affected by treatment assignment and therefore can be used just as any pretreatment covariate, such as age category. As a result, the central property of our principal effects is that they are always causal effects and do not suffer front the complications of standard posttreatment-adjusted estimands. We discuss briefly that such principal causal effects are the link between three recent applications with adjustment for posttreatment variables: (i) treatment noncompliance, (ii) missing outcomes (dropout) following treatment noncompliance, and (iii) censoring by death. We then attack the problem of surrogate or biomarker endpoints, where we show, using principal causal effects, that all current definitions of surrogacy, even when perfectly true, do not generally have the desired interpretation as causal effects of treatment on outcome. We go on to formulate estimands based on principal stratification and principal causal effects and show their superiority.