Composite scores are useful in providing insights and trends about complex and multidimensional quality of care processes. However, missing data in subcomponents may hinder the overall reliability of a composite measure. In this study, strategies for handling missing data in Paediatric Admission Quality of Care (PAQC) score, an ordinal composite outcome, were explored through a simulation study. Specifically, the implications of the conventional method employed in addressing missing PAQC score subcomponents, consisting of scoring missing PAQC score components with a zero, and a multiple imputation (MI)-based strategy, were assessed. The latent normal joint modelling MI approach was used for the latter. Across simulation scenarios, MI of missing PAQC score elements at item level produced minimally biased estimates compared to the conventional method. Moreover, regression coefficients were more prone to bias compared to standards errors. Magnitude of bias was dependent on the proportion of missingness and the missing data generating mechanism. Therefore, incomplete composite outcome subcomponents should be handled carefully to alleviate potential for biased estimates and misleading inferences. Further research on other strategies of imputing at the component and composite outcome level and imputing compatibly with the substantive model in this setting, is needed.

综合评分有助于提供有关复杂和多维护理流程质量的见解和趋势。然而，子组件中的数据缺失可能会影响综合测量的整体可靠性。在本研究中，通过模拟研究探讨了处理儿科入院护理质量 (PAQC) 评分（序数综合结果）中缺失数据的策略。具体来说，评估了用于解决缺失 PAQC 分数子组件的传统方法的影响，包括用零对缺失的 PAQC 分数组件进行评分，以及基于多重插补 (MI) 的策略。后者使用潜在正常关节建模 MI 方法。在整个模拟场景中，与传统方法相比，项目级别缺失 PAQC 评分元素的 MI 产生的偏差最小。此外，与标准误差相比，回归系数更容易出现偏差。偏差的大小取决于缺失的比例和缺失数据的生成机制。因此，应谨慎处理不完整的复合结果子组成部分，以减轻出现偏差估计和误导性推论的可能性。需要进一步研究在成分和综合结果层面上进行估算的其他策略，以及在这种情况下与实质性模型相兼容的估算。