AnacletoLab, Department of Computer Science "Giovanni degli Antoni", Università degli Studi di Milano, Milan, Italy; CINI, Infolife National Laboratory, Roma, Italy; Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA.
J Biomed Inform. 2023 Mar;139:104295. doi: 10.1016/j.jbi.2023.104295. Epub 2023 Jan 27.
Healthcare datasets obtained from Electronic Health Records have proven to be extremely useful for assessing associations between patients' predictors and outcomes of interest. However, these datasets often suffer from missing values in a high proportion of cases, whose removal may introduce severe bias. Several multiple imputation algorithms have been proposed to attempt to recover the missing information under an assumed missingness mechanism. Each algorithm presents strengths and weaknesses, and there is currently no consensus on which multiple imputation algorithm works best in a given scenario. Furthermore, the selection of each algorithm's parameters and data-related modeling choices are also both crucial and challenging. In this paper we propose a novel framework to numerically evaluate strategies for handling missing data in the context of statistical analysis, with a particular focus on multiple imputation techniques. We demonstrate the feasibility of our approach on a large cohort of type-2 diabetes patients provided by the National COVID Cohort Collaborative (N3C) Enclave, where we explored the influence of various patient characteristics on outcomes related to COVID-19. Our analysis included classic multiple imputation techniques as well as simple complete-case Inverse Probability Weighted models. Extensive experiments show that our approach can effectively highlight the most promising and performant missing-data handling strategy for our case study. Moreover, our methodology allowed a better understanding of the behavior of the different models and of how it changed as we modified their parameters. Our method is general and can be applied to different research fields and on datasets containing heterogeneous types.
从电子健康记录中获取的医疗保健数据集已被证明对于评估患者预测因素与感兴趣的结果之间的关联非常有用。然而，这些数据集通常在很大比例的情况下存在缺失值，删除这些缺失值可能会引入严重的偏差。已经提出了几种多重插补算法来尝试根据假定的缺失机制恢复缺失信息。每种算法都有其优点和缺点，目前对于哪种多重插补算法在给定场景下效果最好还没有共识。此外，每个算法的参数选择和与数据相关的建模选择也同样至关重要和具有挑战性。在本文中，我们提出了一种新的框架，用于数值评估在统计分析背景下处理缺失数据的策略，特别是多重插补技术。我们在由国家 COVID 队列协作（N3C）飞地提供的大型 2 型糖尿病患者队列上展示了我们方法的可行性，我们在其中探索了各种患者特征对与 COVID-19 相关的结果的影响。我们的分析包括经典的多重插补技术以及简单的完全病例逆概率加权模型。广泛的实验表明，我们的方法可以有效地突出最有前途和表现最好的缺失数据处理策略。此外，我们的方法允许更好地理解不同模型的行为以及随着参数的修改而如何改变。我们的方法是通用的，可以应用于不同的研究领域和包含异构类型的数据集。
