He Zhe, Wang Shuang, Borhanian Elhaam, Weng Chunhua
Department of Biomedical Informatics, Columbia University, New York, NY, USA.
Department of Biostatistics, Columbia University, New York, NY, USA.
Stud Health Technol Inform. 2015;216:569-73.
Randomized controlled trials generate high-quality medical evidence. However, the use of unjustified inclusion/exclusion criteria may compromise the external validity of a study. We have introduced a method to assess the population representativeness of related clinical trials using electronic health record (EHR) data. As EHR data may not perfectly represent the real-world patient population, in this work, we further validated the method and its results using the National Health and Nutrition Examination Survey (NHANES) data. We visualized and quantified the differences in the distributions of age, HbA1c, and BMI among the target population of Type 2 diabetes trials, diabetics in NHANES databases, and a convenience sample of patients enrolled in selected Type 2 diabetes trials. The results are consistent with the previous study.
随机对照试验能产生高质量的医学证据。然而，使用不合理的纳入/排除标准可能会损害研究的外部效度。我们引入了一种利用电子健康记录（EHR）数据评估相关临床试验人群代表性的方法。由于电子健康记录数据可能无法完美代表真实世界的患者群体，在这项研究中，我们使用美国国家健康与营养检查调查（NHANES）数据进一步验证了该方法及其结果。我们对2型糖尿病试验的目标人群、NHANES数据库中的糖尿病患者以及入选的2型糖尿病试验患者的便利样本之间的年龄、糖化血红蛋白（HbA1c）和体重指数（BMI）分布差异进行了可视化和量化。结果与之前的研究一致。
