Grayling Michael J, Bigirumurame Theophile, Cherlin Svetlana, Ouma Luke, Zheng Haiyan, Wason James M S
Population Health Sciences Institute, Newcastle University, Baddiley-Clark Building, Richardson Road, Newcastle upon Tyne, NE2 4AX, UK.
MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.
BMC Rheumatol. 2021 Jul 2;5(1):21. doi: 10.1186/s41927-021-00192-5.
Despite progress that has been made in the treatment of many immune-mediated inflammatory diseases (IMIDs), there remains a need for improved treatments. Randomised controlled trials (RCTs) provide the highest form of evidence on the effectiveness of a potential new treatment regimen, but they are extremely expensive and time consuming to conduct. Consequently, much focus has been given in recent years to innovative design and analysis methods that could improve the efficiency of RCTs. In this article, we review the current use and future potential of these methods within the context of IMID trials.
We provide a review of several innovative methods that would provide utility in IMID research. These include novel study designs (adaptive trials, Sequential Multi-Assignment Randomised Trials, basket, and umbrella trials) and data analysis methodologies (augmented analyses of composite responder endpoints, using high-dimensional biomarker information to stratify patients, and emulation of RCTs from routinely collected data). IMID trials are now well-placed to embrace innovative methods. For example, well-developed statistical frameworks for adaptive trial design are ready for implementation, whilst the growing availability of historical datasets makes the use of Bayesian methods particularly applicable. To assess whether and how these innovative methods have been used in practice, we conducted a review via PubMed of clinical trials pertaining to any of 51 IMIDs that were published between 2018 and 20 in five high impact factor clinical journals.
Amongst 97 articles included in the review, 19 (19.6%) used an innovative design method, but most of these were relatively straightforward examples of innovative approaches. Only two (2.1%) reported the use of evidence from routinely collected data, cohorts, or biobanks. Eight (9.2%) collected high-dimensional data.
Application of innovative statistical methodology to IMID trials has the potential to greatly improve efficiency, to generalise and extrapolate trial results, and to further personalise treatment strategies. Currently, such methods are infrequently utilised in practice. New research is required to ensure that IMID trials can benefit from the most suitable methods.
尽管在许多免疫介导的炎症性疾病（IMID）的治疗方面已取得进展，但仍需要改进治疗方法。随机对照试验（RCT）提供了关于潜在新治疗方案有效性的最高证据形式，但开展此类试验极其昂贵且耗时。因此，近年来人们将大量精力放在了可提高RCT效率的创新设计和分析方法上。在本文中，我们在IMID试验的背景下回顾这些方法的当前应用情况及其未来潜力。
我们对几种在IMID研究中具有实用价值的创新方法进行了综述。这些方法包括新颖的研究设计（适应性试验、序贯多分配随机试验、篮子试验和伞形试验）以及数据分析方法（对复合缓解终点进行增强分析、利用高维生物标志物信息对患者进行分层，以及从常规收集的数据中模拟RCT）。IMID试验现在已具备采用创新方法的良好条件。例如，适应性试验设计的完善统计框架已准备好实施，而历史数据集的日益丰富使得贝叶斯方法的应用尤为适用。为了评估这些创新方法是否以及如何在实践中得到应用，我们通过PubMed对2018年至2020年间在五种高影响因子临床杂志上发表的与51种IMID中的任何一种相关的临床试验进行了综述。
在纳入综述的97篇文章中，19篇（19.6%）使用了创新设计方法，但其中大多数是创新方法的相对简单示例。只有两篇（2.1%）报告使用了来自常规收集的数据、队列或生物样本库的证据。八篇（9.2%）收集了高维数据。
将创新统计方法应用于IMID试验有可能极大地提高效率、推广和外推试验结果，并进一步使治疗策略个性化。目前，此类方法在实践中很少被使用。需要开展新的研究以确保IMID试验能够受益于最合适的方法。
