Vanderbilt University School of Medicine, 2525 West End Ave Suite 11, Nashville, TN, USA.
Critical Illness, Brain Dysfunction, and Survivorship (CIBS) Center, Vanderbilt University Medical Center, Nashville, TN, USA.
BMC Med Res Methodol. 2021 May 6;21(1):97. doi: 10.1186/s12874-021-01274-1.
In longitudinal critical care studies, researchers may be interested in summarizing an exposure over time and evaluating its association with a long-term outcome. For example, the number of days a patient has delirium (i.e., brain dysfunction) during their critical care stay is associated with the presence and severity of long-term cognitive problems. In large pragmatic trials and multicenter observational studies, particularly when electronic medical record data is used, the information on daily exposure status may be available at some time points and not at others. Model-based multiple imputation is a well-established, widely adopted method to deal with missing data. But the uncertainty around multiple imputation for summary exposure variables is whether the imputation is to be performed at the summary level or at the daily assessment level.
We compare the following approaches to imputing and summarizing partially missing longitudinal data: 1) active imputation, where we impute the summary; 2) passive imputation, where we impute the daily missing data, and then compute the summary; 3) ad hoc methods where we assume all missing time points have the a) most or the b) least extreme value; and 4) complete case analysis where only participants with complete data are analyzed. These methods were applied under different missingness mechanisms, varying proportions of missingness, and association of missingness with an auxiliary variable using simulations that closely mirrors real-life critical care data to be relevant to real-world clinical practice. The performance of the approaches were compared using bias of the estimated coefficients, standard error of the estimate and coverage. We also apply these imputation strategies to two datasets in critical care.
Simulations show that all methods performed comparably when the proportion of missingness was small, indicating that in such instances, the gain over using any imputation model is minimal. But as the proportion of missingness increases, the passive imputation approach provides efficient and less biased estimates under the missingness at random and missingness completely at random mechanism.
For longitudinal data where a summary exposure is of interest, we recommend practitioners adopting the passive imputation strategy.
在纵向重症监护研究中，研究人员可能有兴趣总结一段时间内的暴露情况，并评估其与长期结局的关系。例如，患者在重症监护期间出现谵妄（即大脑功能障碍）的天数与长期认知问题的存在和严重程度有关。在大型实用试验和多中心观察性研究中，特别是当使用电子病历数据时，关于每日暴露情况的信息可能在某些时间点可用，而在其他时间点不可用。基于模型的多重插补是一种成熟且广泛采用的处理缺失数据的方法。但是，对于汇总暴露变量的缺失数据进行多重插补的不确定性在于，插补是在汇总水平上进行还是在每日评估水平上进行。
我们比较了以下几种用于插补和汇总部分缺失纵向数据的方法：1）主动插补，即在汇总水平上进行插补；2）被动插补，即在每日缺失数据上进行插补，然后计算汇总值；3）特定方法，假设所有缺失时间点都具有 a）最极端或 b）最不极端的值；4）完全案例分析，仅对具有完整数据的参与者进行分析。这些方法在不同的缺失机制、缺失比例和缺失与辅助变量的关联下进行了应用，模拟结果与现实生活中的重症监护数据非常相似，与实际临床实践相关。我们使用估计系数的偏差、估计的标准误差和覆盖范围来比较这些方法的性能。我们还将这些插补策略应用于重症监护中的两个数据集。
模拟结果表明，当缺失比例较小时，所有方法的性能都相当，这表明在这种情况下，使用任何插补模型的收益最小。但是，随着缺失比例的增加，在随机缺失和完全随机缺失机制下，被动插补方法提供了高效且偏差较小的估计值。
对于有汇总暴露量的纵向数据，我们建议从业者采用被动插补策略。
