Huang Feiqing, Hou Jue, Zhou Ningxuan, Greco Kimberly, Lin Chenyu, Sweet Sara Morini, Wen Jun, Shen Lechen, Gonzalez Nicolas, Zhang Sinian, Liao Katherine P, Cai Tianrun, Xia Zongqi, Bourgeois Florence T, Cai Tianxi
Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, United States.
Harvard-MIT Center for Regulatory Science, Harvard Medical School, Boston, MA, United States.
J Med Internet Res. 2025 May 12;27:e71873. doi: 10.2196/71873.
Managing chronic diseases requires ongoing monitoring of disease activity and therapeutic responses to optimize treatment plans. With the growing availability of disease-modifying therapies, it is crucial to investigate comparative effectiveness and long-term outcomes beyond those available from randomized clinical trials. We introduce a comprehensive pipeline for generating reproducible and generalizable real-world evidence on disease outcomes by leveraging electronic health record data. The pipeline first generates scalable disease outcomes by linking electronic health record data with registry data containing a small sample of labeled outcomes. It then applies causal analysis using these scalable outcomes to evaluate therapies for chronic diseases. The implementation of the pipeline is illustrated in a case study based on multiple sclerosis. Our approach addresses challenges in real-world evidence generation for disease activity of chronic conditions, specifically the lack of direct observations on key outcomes and biases arising from imperfect or incomplete data. We present advanced machine learning techniques such as semisupervised and ensemble methods to impute missing outcome data, further incorporating steps for calibrated causal analyses and bias correction.
管理慢性病需要持续监测疾病活动和治疗反应，以优化治疗方案。随着疾病修正疗法的日益普及，研究超出随机临床试验可得结果的比较有效性和长期结局至关重要。我们引入了一个综合流程，通过利用电子健康记录数据来生成关于疾病结局的可重复且可推广的真实世界证据。该流程首先通过将电子健康记录数据与包含少量标记结局样本的登记数据相链接来生成可扩展的疾病结局。然后，使用这些可扩展的结局进行因果分析，以评估慢性病的治疗方法。基于多发性硬化症的案例研究说明了该流程的实施情况。我们的方法解决了慢性病疾病活动真实世界证据生成中的挑战，特别是缺乏对关键结局的直接观察以及因数据不完美或不完整而产生的偏差。我们提出了先进的机器学习技术，如半监督和集成方法，以插补缺失的结局数据，并进一步纳入校准因果分析和偏差校正步骤。
