Ouma Luke O, Wason James M S, Zheng Haiyan, Wilson Nina, Grayling Michael
Biostatistics Research Group, Population Health Sciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom.
Medical Research Council (MRC) Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom.
Front Med (Lausanne). 2022 Oct 12;9:1037439. doi: 10.3389/fmed.2022.1037439. eCollection 2022.
The efficiencies that master protocol designs can bring to modern drug development have seen their increased utilization in oncology. Growing interest has also resulted in their consideration in non-oncology settings. Umbrella trials are one class of master protocol design that evaluates multiple targeted therapies in a single disease setting. Despite the existence of several reviews of master protocols, the statistical considerations of umbrella trials have received more limited attention.
We conduct a systematic review of the literature on umbrella trials, examining both the statistical methods that are available for their design and analysis, and also their use in practice. We pay particular attention to considerations for umbrella designs applied outside of oncology.
We identified 38 umbrella trials. To date, most umbrella trials have been conducted in early phase settings (73.7%, 28/38) and in oncology (92.1%, 35/38). The quality of statistical information available about conducted umbrella trials to date is poor; for example, it was impossible to ascertain how sample size was determined in the majority of trials (55.3%, 21/38). The literature on statistical methods for umbrella trials is currently sparse.
Umbrella trials have potentially great utility to expedite drug development, including outside of oncology. However, to enable lessons to be effectively learned from early use of such designs, there is a need for higher-quality reporting of umbrella trials. Furthermore, if the potential of umbrella trials is to be realized, further methodological research is required.
主方案设计给现代药物研发带来的效率提升，已使其在肿瘤学领域的应用日益增加。人们对其兴趣日增，也开始在非肿瘤领域考虑使用。伞式试验是主方案设计的一种，在单一疾病背景下评估多种靶向疗法。尽管已有多篇关于主方案的综述，但伞式试验的统计学考量受到的关注较为有限。
我们对有关伞式试验的文献进行了系统综述，考察了可用于其设计和分析的统计方法，以及它们在实际中的应用。我们特别关注在肿瘤学以外应用的伞式设计的考量因素。
我们识别出38项伞式试验。迄今为止，大多数伞式试验是在早期阶段开展的（73.7%，28/38），且是在肿瘤学领域（92.1%，35/38）。目前已开展的伞式试验所提供的统计信息质量较差；例如，在大多数试验中（55.3%，21/38）无法确定样本量是如何确定的。目前关于伞式试验统计方法的文献较为稀少。
伞式试验在加速药物研发方面具有潜在的巨大效用，包括在肿瘤学以外的领域。然而，为了能从这类设计的早期应用中有效吸取经验教训，需要提高伞式试验的报告质量。此外，若要实现伞式试验的潜力，还需要进一步开展方法学研究。
