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Assessing the Collective Population Representativeness of Related Type 2 Diabetes Trials by Combining Public Data from ClinicalTrials.gov and NHANES

机译:通过将公共数据组合来自Clinicaltrials.gov和Nhanes,评估相关类型2糖尿病试验的集体人口代表性

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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, HbAlc, 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.
机译:随机对照试验产生高质量的医学证据。然而,使用不合理的包含/排除标准可能会损害研究的外部有效性。我们已经介绍了一种使用电子健康记录(EHR)数据评估相关临床试验的人口代表性的方法。由于EHR数据可能无法完美地代表现实世界患者人口,在这项工作中,我们进一步验证了使用国家健康和营养考试调查(NHANES)数据的方法及其结果。我们可视化并量化了2型糖尿病试验的目标人群,Nhanes数据库糖尿病患者的年龄,HBALC和BMI分布的差异,以及患有选定的2型糖尿病试验的患者的便利样本。结果与前一项研究一致。

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