医学
冲程(发动机)
康复
干预(咨询)
物理医学与康复
物理疗法
临床试验
临床神经学
芯(光纤)
试点试验
随机对照试验
神经科学
病理
精神科
材料科学
复合材料
工程类
生物
机械工程
作者
Kathryn S. Hayward,Emily J Dalton,Jessica Barth,Marian Brady,Leora R. Cherney,Leonid Churilov,Andrew N. Clarkson,Jesse Dawson,Sean P. Dukelow,Peter Feys,Maree L. Hackett,Steve R Zeiler,Catherine E. Lang
标识
DOI:10.1177/17474930231199336
摘要
Control comparator selection is a critical trial design issue. Preclinical and clinical investigators who are doing trials of stroke recovery and rehabilitation interventions must carefully consider the appropriateness and relevance of their chosen control comparator as the benefit of an experimental intervention is established relative to a comparator. Establishing a strong rationale for a selected comparator improves the integrity of the trial and validity of its findings. This Stroke Recovery and Rehabilitation Roundtable (SRRR) taskforce used a graph theory voting system to rank the importance and ease of addressing challenges during control comparator design. "Identifying appropriate type of control" was ranked easy to address and very important, "variability in usual care" was ranked hard to address and of low importance, and "understanding the content of the control and how it differs from the experimental intervention" was ranked very important but not easy to address. The CONtrol DeSIGN (CONSIGN) decision support tool was developed to address the identified challenges and enhance comparator selection, description, and reporting. CONSIGN is a web-based tool inclusive of seven steps that guide the user through control comparator design. The tool was refined through multiple rounds of pilot testing that included more than 130 people working in neurorehabilitation research. Four hypothetical exemplar trials, which span preclinical, mood, aphasia, and motor recovery, demonstrate how the tool can be applied in practice. Six consensus recommendations are defined that span research domains, professional disciplines, and international borders.
科研通智能强力驱动
Strongly Powered by AbleSci AI