An important strategy for identifying principal causal effects, which are often used in settings with noncompliance, is to invoke the principal ignorability (PI) assumption which equates unobserved principal-stratum-specific outcome distributions (or their means). As PI is untestable, it is important to gauge how sensitive effect estimates are to its violation. We focus on this task for the common one-sided noncompliance setting where there are two principal strata (compliers and noncompliers), and consider two sensitivity analysis approaches anchoring on and deviating from the mean version and the distribution version of PI. In the mean-based approach, we propose a range of sensitivity parameters (suitable for different outcome types) representing how (within levels of covariates) the mean of potential outcome under control (Y0) is assumed to differ between compliers and noncompliers. In the distribution-based approach, we introduce association between Y0 and principal stratum (within levels of covariates) via a monotonic mapping between Y0 and the probability of belonging to the stratum given covariates and Y0. The mean-based approach is simpler, but can suffer from out-of-range prediction. The distribution-based approach requires additional modeling but avoids this problem. With a view to facilitate practice, the paper offers pairings of sensitivity analysis with several types of main analysis methods. We illustrate the proposed methods using different outcomes from the JOBS II study, and provide code in an R-package.

翻译：识别主成分因果效应（常用于处理非依从性场景）的一项重要策略是假设主成分可忽略性（Principal Ignorability, PI），该假设将未观测的主成分特异结果分布（或其均值）等同起来。由于PI无法直接检验，评估效应估计对其违反的敏感程度至关重要。本文针对常见的单侧非依从性设置（即存在两个主成分：依从者和非依从者）展开研究，并考虑两种敏感性分析方法：分别以PI的均值版本和分布版本为基准进行偏离分析。在基于均值的方法中，我们提出一系列适用于不同结果类型的敏感性参数，用以刻画（在协变量水平内）依从者与非依从者在对照条件下潜在结果Y0的均值差异。在基于分布的方法中，通过Y0与主成分之间的单调映射（给定协变量和Y0后，Y0与属于某一成分的概率之间的关联），引入Y0与主成分之间的关联（在协变量水平内）。基于均值的方法较为简单，但可能出现预测值超出合理范围的问题。基于分布的方法需要额外建模，但避免了这一缺陷。为促进实际应用，本文提供了敏感性分析与多种主流分析方法的配对方案。我们利用JOBS II研究的不同结果数据展示了所提出方法，并提供了R语言包的代码。