Background: Days Alive and at Home (DAH) over a pre-defined follow-up period is a novel post-intervention composite outcome that combines data from at least three components: (i) initial length of hospital stay, (ii) length of total readmissions or other post-discharge care and (iii) mortality. Missing values bring unique challenges to the analysis of trials with the DAH outcome as the three components may have different rates of missingness caused by distinct missing data mechanisms. Current approaches define DAH as missing if any of the components are missing, and proceed with complete cases or Multiple Imputation (MI) of the composite. Methods: Through a simulation study motivated by the NOTACS trial, we compare several methods of handling missing data, including complete case analysis, MI of the composite, and MI of the components when the primary analysis is a Mann-Whitney-Wilcoxon test. Results: MI on the component level has good properties in terms of type I error control and power. We caution against the use of MI on the composite level with Predictive Mean Matching, which can lead to type I error inflation. Conclusions: Given the complex distributional characteristics of DAH, naive approaches such as defining missingness on the composite level and directly imputing the composite with Predictive Mean Matching, can lead to type I error inflation. Imputing on the component level is recommended, suggested future work included imputation approaches that are compatible with more complex definitions of DAH, as well as recommendations for sensitivity analyses to the Missing at Random assumption.

翻译：背景：在预设随访期内，“存活且居家天数”（Days Alive and Home, DAH）是一种新型的干预后复合结局指标，其整合了至少三个组成部分的数据：（i）初始住院时长，（ii）再入院或出院后护理总时长，以及（iii）死亡率。缺失值给以DAH为结局的试验分析带来了独特挑战，因为三个组成部分可能因不同的缺失数据机制而具有不同的缺失率。当前方法将任一组成部分缺失即定义为DAH缺失，并采用完整病例分析或对复合指标进行多重插补（MI）。方法：通过一项以NOTACS试验为动机的模拟研究，我们比较了多种处理缺失数据的方法，包括完整病例分析、对复合指标的MI、以及对组成部分的MI，其中主要分析采用Mann-Whitney-Wilcoxon检验。结果：在组成部分水平进行MI在Ⅰ类错误控制和检验效能方面表现良好。我们提醒避免在复合指标水平使用预测均值匹配进行MI，这可能导致Ⅰ类错误膨胀。结论：鉴于DAH复杂的分布特征，例如在复合指标水平定义缺失并直接用预测均值匹配插补复合指标等简单方法可能导致Ⅰ类错误膨胀。建议在组成部分水平进行插补，未来工作需开发与更复杂DAH定义兼容的插补方法，并提出对随机缺失假设进行敏感性分析的建议。