The ISNI (index of sensitivity to local nonignorability) method quantifies local sensitivity of parametric inferences to nonignorable missingness in an outcome variable. Here we extend ISNI to the situations where both outcomes and predictors can be missing and where the missingness mechanism can be either parametric or semi-parametric. We define the quantity MinNI (minimum nonignorability) to be an approximation to the norm of the smallest value of the transformed nonignorability that gives a nonnegligible displacement of the estimate of the parameter of interest. We illustrate our method in a complete data set from which we synthetically delete observations according to various patterns. We then apply the method to real-data examples involving the normal linear model and conditional logistic regression.

中文翻译：

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具有缺失结果和预测因子的局部对不可忽略性的敏感性分析

ISNI（对局部不可忽略性的敏感性指数）方法量化参数推断对结果变量中不可忽略缺失的局部敏感性。在这里，我们将 ISNI 扩展到结果和预测变量都可能缺失以及缺失机制可以是参数或半参数的情况。我们将数量MinNI（最小不可忽略性）定义为转换后不可忽略性最小值范数的近似值，它给出了感兴趣参数估计值的不可忽略位移。我们在一个完整的数据集中说明了我们的方法，我们从中根据各种模式综合删除了观察结果。然后，我们将该方法应用于涉及正态线性模型和条件逻辑回归的真实数据示例。