Designing Single-Arm Clinical Trials: Principles, Applications, and Methodological Considerations

Single-arm trials (SATs) are clinical studies without a parallel control group, serving as a vital alternative to randomized controlled trials (RCTs) in scenarios where traditional trial designs are impractical. These trials are particularly relevant in rare diseases, advanced malignancies, novel tr...

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Published inAnnals of Clinical Epidemiology Vol. 7; no. 3; pp. 90 - 98
Main Authors Yao, Shuna, Shang, Qingyao, Ouyang, Meishuo, Zhou, Heng, Yao, Zhihua, Liu, Yanyan, Luo, Sheng
Format Journal Article
LanguageEnglish
Published Japan Society for Clinical Epidemiology 01.07.2025
一般社団法人 日本臨床疫学会
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ISSN2434-4338
2434-4338
DOI10.37737/ace.25011

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Summary:Single-arm trials (SATs) are clinical studies without a parallel control group, serving as a vital alternative to randomized controlled trials (RCTs) in scenarios where traditional trial designs are impractical. These trials are particularly relevant in rare diseases, advanced malignancies, novel treatment modalities, and life-threatening conditions, where ethical concerns, logistical challenges, or small patient populations limit the feasibility of RCTs. SATs enable expedited evaluation of therapeutic interventions, often forming the foundation for regulatory approvals.This article explores the principles, applications, and methodological considerations of SATs. Their advantages include smaller sample size requirements, faster timelines, and regulatory acceptance by agencies such as the U.S. Food and Drug Administration (FDA) and European Medicines Agency (EMA). Despite these benefits, SATs face challenges, such as potential biases due to the lack of a control group, limitations in endpoints, and reliance on historical controls that may compromise result validity. Best practices in SAT design are outlined, including refining scientific questions, defining eligibility criteria, selecting clinically meaningful endpoints, and employing robust statistical methods like Simon’s two-stage design and Bayesian approaches.
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ISSN:2434-4338
2434-4338
DOI:10.37737/ace.25011