Centre of Research in Epidemiology and Statistics, Université de Paris, Paris, Île-de-France, France
Centre of Research in Epidemiology and Statistics, Université de Paris, Paris, Île-de-France, France.
BMJ Open. 2022 May 6;12(5):e052926. doi: 10.1136/bmjopen-2021-052926.
Personalised medicine (PM) allows treating patients based on their individual demographic, genomic or biological characteristics for tailoring the 'right treatment for the right person at the right time'. Robust methodology is required for PM clinical trials, to correctly identify groups of participants and treatments. As an initial step for the development of new recommendations on trial designs for PM, we aimed to present an overview of the study designs that have been used in this field.
Scoping review.
We searched (April 2020) PubMed, Embase and the Cochrane Library for all reports in English, French, German, Italian and Spanish, describing study designs for clinical trials applied to PM. Study selection and data extraction were performed in duplicate resolving disagreements by consensus or by involving a third expert reviewer. We extracted information on the characteristics of trial designs and examples of current applications of these approaches. The extracted information was used to generate a new classification of trial designs for PM.
We identified 21 trial designs, 10 subtypes and 30 variations of trial designs applied to PM, which we classified into four core categories (namely, Master protocol, Randomise-all, Biomarker strategy and Enrichment). We found 131 clinical trials using these designs, of which the great majority were master protocols (86/131, 65.6%). Most of the trials were phase II studies (75/131, 57.2%) in the field of oncology (113/131, 86.3%). We identified 34 main features of trial designs regarding different aspects (eg, framework, control group, randomisation). The four core categories and 34 features were merged into a double-entry table to create a new classification of trial designs for PM.
A variety of trial designs exists and is applied to PM. A new classification of trial designs is proposed to help readers to navigate the complex field of PM clinical trials.
个性化医学（PM）允许根据患者的个体人口统计学、基因组或生物学特征进行治疗，以实现“在正确的时间为正确的人提供正确的治疗”。PM 临床试验需要稳健的方法，以正确识别参与者和治疗组。作为制定 PM 临床试验新建议的初始步骤，我们旨在概述该领域中使用的研究设计。
范围综述。
我们在 2020 年 4 月在 PubMed、Embase 和 Cochrane 图书馆中搜索了所有以英文、法文、德文、意大利文和西班牙文描述应用于 PM 的临床试验设计的报告。研究选择和数据提取由两名研究员进行，解决分歧的方法是协商一致或请第三名专家评审员参与。我们提取了试验设计特征和这些方法的当前应用实例的信息。所提取的信息用于生成 PM 的新试验设计分类。
我们确定了 21 种试验设计、10 种亚型和 30 种 PM 应用的试验设计变体，将其分为四个核心类别（即主方案、随机化全部、生物标志物策略和富集）。我们发现了 131 项使用这些设计的临床试验，其中绝大多数是主方案（86/131，65.6%）。大多数试验是肿瘤学领域的 II 期研究（75/131，57.2%）（113/131，86.3%）。我们确定了 34 项关于不同方面（例如，框架、对照组、随机化）的试验设计的主要特征。四个核心类别和 34 个特征被合并到一个双输入表中，以创建一个新的 PM 临床试验设计分类。
存在多种试验设计并应用于 PM。提出了一种新的试验设计分类，以帮助读者了解 PM 临床试验的复杂领域。
