Mattei Alessandra, Mealli Fabrizia, Pacini Barbara
Department of Statistics, Informatics, Applications, University of Florence, Florence, Italy.
Biometrics. 2014 Jun;70(2):278-88. doi: 10.1111/biom.12136. Epub 2014 Jan 21.
We consider a new approach to identify the causal effects of a binary treatment when the outcome is missing on a subset of units and dependence of nonresponse on the outcome cannot be ruled out even after conditioning on observed covariates. We provide sufficient conditions under which the availability of a binary instrument for nonresponse allows us to derive tighter identification intervals for causal effects in the whole population and to partially identify causal effects in some latent subgroups of units, named Principal Strata, defined by the nonresponse behavior in all possible combinations of treatment and instrument. A simulation study is used to assess the benefits of the presence versus the absence of an instrument for nonresponse. The simulation design is based on real health data, coming from a randomized trial on breast self-examination (BSE) affected by a large proportion of missing outcome data. An instrument for nonresponse is simulated considering alternative scenarios to discuss the key role of the instrument for nonresponse in identifying average causal effects in presence of nonignorable missing outcomes. We also investigate the potential inferential gains from using an instrument for nonresponse adopting a Bayesian approach for inference. In virtue of our theoretical and empirical results, we provide some recommendations on study designs for causal inference.
当一部分个体的结果数据缺失，且即使在对观测到的协变量进行条件设定后也不能排除无应答与结果之间的依赖性时，我们考虑一种新方法来识别二元处理的因果效应。我们给出了充分条件，在这些条件下，二元无应答工具的可用性使我们能够为总体中的因果效应得出更窄的识别区间，并部分识别一些潜在个体子组（称为主要分层）中的因果效应，这些子组由处理和工具的所有可能组合中的无应答行为定义。我们通过一项模拟研究来评估有无无应答工具的益处。模拟设计基于真实健康数据，该数据来自一项关于乳房自我检查（BSE）的随机试验，该试验受到大量结果数据缺失的影响。我们模拟了无应答工具，并考虑了不同的情景，以讨论无应答工具在存在不可忽视的缺失结果时识别平均因果效应中的关键作用。我们还采用贝叶斯推断方法研究了使用无应答工具可能带来的推断收益。基于我们的理论和实证结果，我们对因果推断的研究设计提出了一些建议。
