Wang Xuzhi, Larson Martin G, Liu Chunyu
Department of Biostatistics, Boston University School of Public Health, Boston, MA, 02118, USA.
BMC Med Res Methodol. 2025 May 6;25(1):124. doi: 10.1186/s12874-025-02555-9.
Missing data are common in longitudinal studies. Multiple imputation (MI) is widely used to handle missing data. However, most of the MI methods assume various missing data types as missing at random (MAR) in imputation. Two-stage MI is a flexible method that accounts for two types of missing data in a two-step process, allowing researchers to employ diverse assumptions regarding the mechanisms underlying the missing data. This method has immense potential yet limited application and extension within the field.
We evaluated the performance of two-stage MI in a novel context, imputing a composite variable constructed from several continuous and binary components in the longitudinal setting while handling missing data due to MAR and missing not at random (MNAR). Additionally, we compared three fully conditional specification (FCS) methods within the two-stage MI framework. Simulation studies were conducted using a longitudinal dataset that mimicked a cohort study. Sensitivity analysis was performed with various ignorability assumptions.
In simulation studies, the imputation models within two-stage MI, assuming appropriate ignorability assumptions, exhibited the smallest bias and achieved optimal coverage probabilities for the means, slopes across different time points, and hazard ratios for mortality related to the composite variable. The FCS methods that incorporated longitudinal information yielded the best performance in most scenarios.
In the context of a longitudinal composite variable with missing values due to various missing mechanisms, the selection of imputation methods and ignorability assumptions plays an important role within the two-stage MI framework.
缺失数据在纵向研究中很常见。多重填补（MI）被广泛用于处理缺失数据。然而，大多数MI方法在填补时将各种缺失数据类型假定为随机缺失（MAR）。两阶段MI是一种灵活的方法，它在两步过程中考虑两种类型的缺失数据，使研究人员能够对缺失数据背后的机制采用不同的假设。该方法具有巨大潜力，但在该领域内的应用和扩展有限。
我们在一个新的背景下评估了两阶段MI的性能，在纵向环境中填补由几个连续和二元成分构成的复合变量，同时处理因MAR和非随机缺失（MNAR）导致的缺失数据。此外，我们在两阶段MI框架内比较了三种完全条件设定（FCS）方法。使用模拟队列研究的纵向数据集进行模拟研究。在各种可忽略性假设下进行敏感性分析。
在模拟研究中，两阶段MI中的填补模型，在假定适当的可忽略性假设时，偏差最小，并且在均值、不同时间点的斜率以及与复合变量相关的死亡率风险比方面实现了最佳的覆盖概率。纳入纵向信息的FCS方法在大多数情况下表现最佳。
在因各种缺失机制而存在缺失值的纵向复合变量的背景下，填补方法和可忽略性假设的选择在两阶段MI框架中起着重要作用。
