倾向得分匹配
随机对照试验
混淆
匹配(统计)
临床试验
贝叶斯概率
医学
控制(管理)
外部有效性
构造(python库)
金标准(测试)
物理医学与康复
计算机科学
统计
外科
内科学
人工智能
数学
病理
程序设计语言
作者
Heinz Schmidli,Dieter A. Häring,Marius Thomas,Adrian Cassidy,Sebastian Weber,Frank Bretz
摘要
Randomized controlled trials are the gold standard to investigate efficacy and safety of new treatments. In certain settings, however, randomizing patients to control may be difficult for ethical or feasibility reasons. Borrowing strength using relevant individual patient data on control from external trials or real-world data (RWD) sources may then allow us to reduce, or even eliminate, the concurrent control group. Naive direct use of external control data is not valid due to differences in patient characteristics and other confounding factors. Instead, we suggest the rigorous application of meta-analytic and propensity score methods to use external controls in a principled way. We illustrate these methods with two case studies: (i) a single-arm trial in a rare cancer disease, using propensity score matching to construct an external control from RWD; (ii) a randomized trial in children with multiple sclerosis, borrowing strength from past trials using a Bayesian meta-analytic approach.
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