Missingness mechanism is in theory unverifiable based only on observed data. If there is a suspicion of missing not at random, researchers often perform a sensitivity analysis to evaluate the impact of various missingness mechanisms. In general, sensitivity analysis approaches require a full specification of the relationship between missing values and missingness probabilities. Such relationship can be specified based on a selection model, a pattern‐mixture model or a shared parameter model. Under the selection modeling framework, we propose a sensitivity analysis approach using a nonparametric multiple imputation strategy. The proposed approach only requires specifying the correlation coefficient between missing values and selection (response) probabilities under a selection model. The correlation coefficient is a standardized measure and can be used as a natural sensitivity analysis parameter. The sensitivity analysis involves multiple imputations of missing values, yet the sensitivity parameter is only used to select imputing/donor sets. Hence, the proposed approach might be more robust against misspecifications of the sensitivity parameter. For illustration, the proposed approach is applied to incomplete measurements of level of preoperative Hemoglobin A1c, for patients who had high‐grade carotid artery stenosisa and were scheduled for surgery. A simulation study is conducted to evaluate the performance of the proposed approach.

中文翻译：

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一种基于多重插补的敏感性分析方法，适用于非随机丢失的数据。

缺失机制在理论上仅根据观测数据是无法验证的。如果怀疑不是随机缺失，研究人员通常会进行敏感性分析来评估各种缺失机制的影响。一般来说，敏感性分析方法需要完整说明缺失值和缺失概率之间的关系。这种关系可以基于选择模型、模式混合模型或共享参数模型来指定。在选择建模框架下，我们提出了一种使用非参数多重插补策略的敏感性分析方法。所提出的方法只需要指定选择模型下缺失值和选择（响应）概率之间的相关系数。相关系数是标准化度量，可用作自然敏感性分析参数。敏感性分析涉及缺失值的多重插补，但敏感性参数仅用于选择插补/供体集。因此，所提出的方法可能对灵敏度参数的错误指定更加稳健。为了说明这一点，所提出的方法适用于术前血红蛋白 A1c 水平的不完整测量，适用于患有高度颈动脉狭窄并计划进行手术的患者。进行模拟研究以评估所提出方法的性能。