Yue Tingyan, Zhang Tao
West China Second University Hospital, Sichuan University, Chengdu, China.
West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China.
BMC Med Inform Decis Mak. 2021 Nov 12;21(1):316. doi: 10.1186/s12911-021-01677-6.
Traditional approaches to identify missing mechanisms are usually based on the hypothesis test and confronted with both theoretical and practical challenges. It has been proved that the Bayesian network is powerful in integrating, analyzing and visualizing information, and some previous researches have verified the promising features of Bayesian network to deal with the aforementioned challenges in missing mechanism identification. Based on the above reasons, this paper explores the application of Bayesian network to the identification of missing mechanisms for the first time, and proposes a new method, the Bayesian network-based missing mechanism identification (BN-MMI) method, to identify missing mechanism in medical research.
The procedure of BN-MMI method consists three easy-to-implement steps: estimating the missing data structure by the Bayesian network; assessing the credibility of the estimated missing data structure; and identifying the missing mechanism from the estimated missing data structure. The BN-MMI method is verified by simulation research and empirical research.
The simulation study verified the validity, consistency and robustness of BN-MMI method, and indicated its outperformance in contrast to the traditional logistic regression method. In addition, the empirical study illustrated the applicability of BN-MMI method in the real world by an example of medical record data.
It was confirmed that the BN-MMI method itself, together with human knowledge and expertise, could identify the missing mechanisms according to the probabilistic dependence/independence relations among variables of interest. At the same time, our research shed light upon the potential application of BN-MMI method to a broader range of missing data issues in medical studies.
识别缺失机制的传统方法通常基于假设检验，面临理论和实践两方面的挑战。事实证明，贝叶斯网络在整合、分析和可视化信息方面具有强大功能，先前的一些研究已经验证了贝叶斯网络在应对缺失机制识别中上述挑战方面具有良好特性。基于上述原因，本文首次探索贝叶斯网络在缺失机制识别中的应用，并提出一种新方法——基于贝叶斯网络的缺失机制识别（BN-MMI）方法，用于医学研究中的缺失机制识别。
BN-MMI方法的步骤包括三个易于实施的步骤：通过贝叶斯网络估计缺失数据结构；评估估计的缺失数据结构的可信度；从估计的缺失数据结构中识别缺失机制。通过模拟研究和实证研究对BN-MMI方法进行验证。
模拟研究验证了BN-MMI方法的有效性、一致性和稳健性，并表明其相对于传统逻辑回归方法的优越性。此外，实证研究通过病历数据实例说明了BN-MMI方法在现实世界中的适用性。
证实了BN-MMI方法本身与人类知识和专业技能相结合，可以根据感兴趣变量之间的概率依赖/独立关系识别缺失机制。同时，我们的研究揭示了BN-MMI方法在医学研究中更广泛的缺失数据问题上的潜在应用。
