医学
临床试验
冲程(发动机)
随机化
提前停车
样本量测定
人口
干预(咨询)
人气
物理疗法
物理医学与康复
重症监护医学
人工智能
统计
内科学
精神科
计算机科学
机械工程
工程类
社会心理学
心理学
数学
环境卫生
人工神经网络
作者
Amy Crawford,Elizabeth Lorenzi,Benjamin R. Saville,Roger Lewis,Craig S. Anderson
出处
期刊:Stroke
[Ovid Technologies (Wolters Kluwer)]
日期:2024-10-22
标识
DOI:10.1161/strokeaha.124.046125
摘要
Designing a clinical trial to evaluate the efficacy of an intervention is often complicated by uncertainty over aspects of the study population, potential treatment effects, most relevant outcomes, dropouts, and other factors. However, once participants begin to be enrolled and partial trial data become available, this level of uncertainty is reduced. Adaptive clinical trials are designed to take advantage of the accumulating data during the conduct of a trial to make changes according to prespecified decision rules to increase the likelihood of success or statistical efficiency. Common adaptive rules address early stopping for benefit or futility, sample size reestimation, adding or dropping treatment arms or altering randomization ratios, and changing the eligibility criteria to focus on responder patient subgroups. Adaptive clinical trials are gaining popularity for clinical stroke research. We provide an overview of the methods, practical considerations, challenges and limitations, and potential future role of adaptive clinical trials in advancing knowledge and practice in stroke.
科研通智能强力驱动
Strongly Powered by AbleSci AI