Although approaches for handling missing data from longitudinal studies are well-developed when the patterns of missingness are monotone, fewer methods are available for non-monotone missingness. Moreover, the conventional missing at random (MAR) assumption -- a natural benchmark for monotone missingness -- does not model realistic beliefs about non-monotone missingness processes (Robins and Gill, 1997). This has provided the impetus for alternative non-monotone missing not at random (MNAR) mechanisms. The "no self-censoring" (NSC) model is such a mechanism and assumes the probability an outcome variable is missing is independent of its value when conditioning on all other possibly missing outcome variables and their missingness indicators. As an alternative to "weighting" methods that become computationally demanding with increasing number of outcome variables, we propose a multiple imputation approach under NSC. We focus on the case of binary outcomes and present results of simulation and asymptotic studies to investigate the performance of the proposed imputation approach. We describe a related approach to sensitivity analysis to departure from NSC. Finally, we discuss the relationship between MAR and NSC and prove that one is not a special case of the other. The proposed methods are illustrated with application to a substance use disorder clinical trial.

翻译：尽管在缺失模式为单调时，处理纵向研究缺失数据的方法已较为成熟，但针对非单调缺失的方法较少。此外，传统的随机缺失假设（MAR）——作为单调缺失的自然基准——并不能模拟对非单调缺失过程的现实信念（Robins and Gill, 1997）。这推动了替代性非单调非随机缺失（MNAR）机制的发展。“无自我删失”（NSC）模型正是这样一种机制，它假设结果变量缺失的概率在以其所有其他可能缺失的结果变量及其缺失指示符为条件时，独立于其自身值。作为对“加权”方法的替代——后者随结果变量数量增加而计算量激增——我们提出了一种基于NSC的多重插补方法。我们聚焦于二值结果情况，并通过模拟研究和渐近分析来考察所提出的插补方法的性能。我们描述了一种用于评估偏离NSC假设的敏感性分析方法。最后，我们讨论了MAR与NSC之间的关系，并证明两者并非彼此的特例。所提出的方法已应用于一项物质使用障碍临床试验。