概化理论
随机对照试验
频数推理
随机化
临床试验
医学
临床研究设计
研究设计
因果推理
医学物理学
贝叶斯概率
计算机科学
心理学
贝叶斯推理
人工智能
统计
数学
外科
发展心理学
病理
作者
John A. Harvin,Ben L. Zarzaur,Raminder Nirula,Benjamin King,Ajai K. Malhotra
标识
DOI:10.1136/tsaco-2019-000420
摘要
High-quality clinical trials are needed to advance the care of injured patients. Traditional randomized clinical trials in trauma have challenges in generating new knowledge due to many issues, including logistical difficulties performing individual randomization, unclear pretrial estimates of treatment effect leading to often unpowered studies, and difficulty assessing the generalizability of an intervention given the heterogeneity of both patients and trauma centers. In this review, we discuss alternative clinical trial designs that can address some of these difficulties. These include pragmatic trials, cluster randomization, cluster randomized stepped wedge designs, factorial trials, and adaptive designs. Additionally, we discuss how Bayesian methods of inference may provide more knowledge to trauma and acute care surgeons compared with traditional, frequentist methods.
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