Zhan Stella Jinran, Saffari Seyed Ehsan, Ong Marcus Eng Hock, Siddiqui Fahad Javaid
Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore 169857, Singapore.
Department of Emergency Medicine, Singapore General Hospital, Singapore 169608, Singapore.
J Clin Med. 2025 Sep 8;14(17):6345. doi: 10.3390/jcm14176345.
Clinical observational studies often encounter missing data, which complicates association evaluation with reduced bias while accounting for confounders. This is particularly challenging in multi-national registries such as those for out-of-hospital cardiac arrest (OHCA), a time-sensitive medical emergency with low survival rates. While various methods for handling missing data exist, observational studies frequently rely on complete-case analysis, limiting representativeness and potentially introducing bias. Our objective was to evaluate the impact of various single imputation methods on association analysis with OHCA registries. Using a complete dataset (N = 13,274) from the Pan-Asian Resuscitation Outcomes Study (PAROS) registry (1 January 2016-31 December 2020) as reference, we intentionally introduced missing values into selected variables via a Missing At Random (MAR) mechanism. We then compared statistical and machine learning (ML) single imputation methods to assess the association between bystander cardiopulmonary resuscitation (BCPR) and the issuance of a mobile app alert, adjusting for confounders. The impacts of complete-case analysis (CCA) and single imputation methods on conclusions in OHCA research were evaluated. CCA was suboptimal for handling MAR data, resulting in more biased estimates and wider confidence intervals compared to single imputation methods. The missingness-indicator (MxI) method offered a trade-off between bias and ease of implementation. The K-Nearest Neighbours (KNN) method outperformed other imputation approaches, whereas missForest introduced bias under certain conditions. KNN and MxI are easy to use and better alternatives to CCA for reducing bias in observational studies. This study highlights the importance of selecting appropriate imputation methods to ensure reliable conclusions in OHCA research and has broader implications for other registries facing similar missing data challenges.
临床观察性研究经常遇到缺失数据的情况，这使得在考虑混杂因素时，关联评估因偏差减少而变得复杂。在多国家注册研究中，这一问题尤为具有挑战性，比如院外心脏骤停（OHCA）注册研究，OHCA是一种对时间敏感的医疗急症，生存率较低。虽然存在多种处理缺失数据的方法，但观察性研究常常依赖于完整病例分析，这限制了代表性并可能引入偏差。我们的目标是评估各种单一插补方法对OHCA注册研究中关联分析的影响。以泛亚复苏结局研究（PAROS）注册研究（2016年1月1日至2020年12月31日）的完整数据集（N = 13,274）为参考，我们通过随机缺失（MAR）机制有意在选定变量中引入缺失值。然后，我们比较了统计和机器学习（ML）单一插补方法，以评估旁观者心肺复苏（BCPR）与移动应用警报发布之间的关联，并对混杂因素进行了调整。评估了完整病例分析（CCA）和单一插补方法对OHCA研究结论的影响。与单一插补方法相比，CCA在处理MAR数据方面并不理想，导致估计偏差更大，置信区间更宽。缺失指示符（MxI）方法在偏差和实施简易性之间提供了一种权衡。K近邻（KNN）方法优于其他插补方法，而missForest在某些条件下会引入偏差。KNN和MxI易于使用，是比CCA更好的减少观察性研究偏差的替代方法。本研究强调了选择合适的插补方法以确保OHCA研究得出可靠结论的重要性，并且对面临类似缺失数据挑战的其他注册研究具有更广泛的意义。
