Data with missing covariate values but fully observed binary outcomes are an important subset of the missing data challenge. Common approaches are complete case analysis (CCA) and multiple imputation (MI). While CCA relies on missing completely at random (MCAR), MI usually relies on a missing at random (MAR) assumption to produce unbiased results. For MI involving logistic regression models, it is also important to consider several missing not at random (MNAR) conditions under which CCA is asymptotically unbiased and, as we show, MI is also valid in some cases. We use a data application and simulation study to compare the performance of several machine learning and parametric MI methods under a fully conditional specification framework (MI-FCS). Our simulation includes five scenarios involving MCAR, MAR, and MNAR under predictable and nonpredictable conditions, where "predictable" indicates missingness is not associated with the outcome. We build on previous results in the literature to show MI and CCA can both produce unbiased results under more conditions than some analysts may realize. When both approaches were valid, we found that MI-FCS was at least as good as CCA in terms of estimated bias and coverage, and was superior when missingness involved a categorical covariate. We also demonstrate how MNAR sensitivity analysis can build confidence that unbiased results were obtained, including under MNAR-predictable, when CCA and MI are both valid. Since the missingness mechanism cannot be identified from observed data, investigators should compare results from MI and CCA when both are plausibly valid, followed by MNAR sensitivity analysis.

中文翻译：

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使用 MNAR 敏感性分析的逻辑回归中缺失协变量数据的方法

缺失协变量值但完全观察到二元结果的数据是缺失数据挑战的一个重要子集。常见的方法是完整案例分析 (CCA) 和多重插补 (MI)。虽然 CCA 依赖于完全随机缺失 (MCAR)，但 MI 通常依赖于随机缺失 (MAR) 假设来产生无偏结果。对于涉及逻辑回归模型的 MI，考虑几个非随机缺失 (MNAR) 条件也很重要，在这些条件下，CCA 是渐近无偏的，并且正如我们所展示的，MI 在某些情况下也是有效的。我们使用数据应用和模拟研究来比较几种机器学习和参数化 MI 方法在完全条件规范框架 (MI-FCS) 下的性能。我们的模拟包括涉及 MCAR、MAR、和 MNAR 在可预测和不可预测条件下，其中“可预测”表示缺失与结果无关。我们以文献中先前的结果为基础，表明 MI 和 CCA 在比一些分析师可能意识到的更多条件下都可以产生无偏见的结果。当两种方法都有效时，我们发现 MI-FCS 在估计偏差和覆盖率方面至少与 CCA 一样好，并且在缺失涉及分类协变量时更优。我们还演示了当 CCA 和 MI 都有效时，MNAR 敏感性分析如何建立对获得无偏结果的信心，包括在 MNAR 可预测的情况下。由于无法从观察到的数据中确定缺失机制，研究人员应该比较 MI 和 CCA 的结果，当两者都合理有效时，