Feasibility Study: A Mathematical Language for Complex Healthcare Interventions

可行性研究：复杂医疗干预的数学语言

• 批准号: EP/W001020/1
• 负责人: Steven Gilmour
• 金额: $ 4.35万
• 依托单位: King's College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FW001020%2F1
• 关键词: Feasibility; Study; Mathematical; Language; Complex

Randomised clinical trials are the gold standard in assessing new, or competing, treatments in healthcare. It is well known that they are used in comparing new pharmaceutical treatments, where the most common type of trial compares a new drug with a standard. However, randomised trials are increasingly widely used in other areas such as psychiatry, surgery and social care. In these areas the healthcare interventions to be assessed are often more complex, being made up of different combinations of components. For example, the effectiveness of a psychiatric intervention might depend on the form in which written material is presented, the length of time of the course of treatment and the therapist. Planning such trials can be more complex than simply randomly allocating subjects to treatments, especially where there are some restrictions in the randomisation, e.g. all patients at one centre might have to see the same therapist, but can be given written material in different forms. Therefore methods for planning such trials and for analysing the data collected from them have to be chosen carefully in order to make sure all the required information is discovered and obtained without bias.Such complex interventions can be classified and described using the mathematical language of the statistical design of experiments. This is widely used in experiments in engineering, laboratory science and elsewhere, but rarely in clinical trials. The aim of this project is to map the different types of clinical trials with complex interventions onto this mathematical framework. This will allow us to identify which methods are needed to design and analyse particular types of trial. These methods will then be translated back into the language understood by clinical triallists, so that they can be adapted to use in future trials.It is expected that this work will lead to us identifying some gaps in the methodology of the design of experiment for some types of clinical trial. This will therefore be used to guide future research in the methodology of the design of experiments, as well as in the application to clinical trials. In the long term, this work will allow healthcare researchers to speed up the testing of new interventions and obtain more robust evidence that they will work in a range of real-life scenarios.

随机临床试验是评估新的或竞争性的医疗保健治疗的黄金标准。众所周知，它们用于比较新的药物治疗，其中最常见的试验类型是将新药与标准品进行比较。然而，随机试验越来越广泛地用于其他领域，如精神病学，外科手术和社会护理。在这些领域，要评估的医疗保健干预措施往往更为复杂，由不同的成分组合组成。例如，精神病干预的有效性可能取决于书面材料的形式，治疗过程的时间长短和治疗师。计划此类试验可能比简单地随机分配受试者接受治疗更复杂，特别是在随机分配中有一些限制的情况下，例如，一个中心的所有患者可能必须看同一位治疗师，但可以提供不同形式的书面材料。因此，必须仔细选择计划此类试验和分析从中收集的数据的方法，以确保发现和获得所有所需的信息，而不存在偏倚。此类复杂的干预措施可以使用实验统计设计的数学语言进行分类和描述。这广泛用于工程、实验室科学和其他地方的实验，但很少用于临床试验。该项目的目的是将具有复杂干预措施的不同类型的临床试验映射到这个数学框架上。这将使我们能够确定需要哪些方法来设计和分析特定类型的试验。这些方法将被翻译成临床试验者能够理解的语言，以便在未来的试验中使用，预计这项工作将使我们确定某些类型临床试验的实验设计方法中的一些差距。因此，这将用于指导未来的研究，在方法学的实验设计，以及在临床试验中的应用。从长远来看，这项工作将使医疗保健研究人员能够加快新干预措施的测试，并获得更有力的证据，证明它们将在一系列现实生活中发挥作用。