Laboratory for Psychiatric Biostatistics, McLean Hospital, Belmont, MA, USA.
Department of Psychiatry, Harvard Medical School, Boston, MA, USA.
Stat Methods Med Res. 2023 Oct;32(10):1973-1993. doi: 10.1177/09622802231188520. Epub 2023 Aug 30.
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 assumption-a natural benchmark for monotone missingness-does not model realistic beliefs about the non-monotone missingness processes (Robins and Gill, 1997). This has provided the impetus for alternative non-monotone missing not at random mechanisms. The "no self-censoring" 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 no self-censoring. 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 no self-censoring. We discuss the relationship between missing at random and no self-censoring and prove that one is not a special case of the other. Finally, we discuss extensions to non-binary data settings. The proposed methods are illustrated with application to a substance use disorder clinical trial.
虽然当缺失模式单调时，处理纵向研究中缺失数据的方法已经很成熟，但对于非单调缺失，可用的方法较少。此外，常规的随机缺失假设——单调缺失的自然基准——并不适合对非单调缺失过程的实际信念（Robins 和 Gill，1997）。这为替代的非单调缺失非随机机制提供了动力。“无自我审查”模型就是这样一种机制，它假设在对所有其他可能缺失的结果变量及其缺失指示符进行条件化时，一个结果变量缺失的概率与其值无关。作为对随着结果变量数量增加而变得计算繁重的“加权”方法的替代方法，我们提出了一种无自我审查下的多重插补方法。我们专注于二项结果的情况，并呈现模拟和渐近研究的结果，以研究拟议的插补方法的性能。我们描述了一种与无自我审查偏离的敏感性分析的相关方法。我们讨论了随机缺失和无自我审查之间的关系，并证明一个不是另一个的特殊情况。最后，我们讨论了对非二进制数据设置的扩展。我们通过应用于物质使用障碍临床试验来说明所提出的方法。
