Assurance methods for adaptive clinical trial designs
适应性临床试验设计的保证方法
基本信息
- 批准号:2610753
- 负责人:
- 金额:--
- 依托单位:
- 依托单位国家:英国
- 项目类别:Studentship
- 财政年份:2021
- 资助国家:英国
- 起止时间:2021 至 无数据
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
When designing a clinical trial, the number of patients recruited to the trial must be planned for carefully. If there are too few, there is a risk that the trial will not provide sufficient evidence that the treatment works. If there are too many, some patients will be needlessly enrolled into a study, potentially receiving a treatment that could, at that time, be demonstrated to be ineffective.Traditionally, to choose the number of patients, the statistical concept of 'power' is used. Informally, the power of a clinical trial is the probability that the trial will be successful and demonstrate that the treatment works. Critically, power assumes the new treatment really does work as well as desired. But we cannot know this to be true before the study starts. In practice, trials are successful significantly less frequently than predicted from power calculations, and this is very costly.This project will develop an alternative statistical method: "assurance" that involves properly assessing uncertainty about the effectiveness of the treatment, before the trial is conducted, so that a more realistic assessment can be made of the chances of success for the trial. Specifically, assurance methods will developed for more complex types of trial design.In the assurance method, the format of the trial and the method for analysing the trial data are first specified, as they would be for a conventional power calculation. We then 'elicit' a probability distribution from experts - supported by available evidence and expertise - to represent uncertainty about the effectiveness of the new treatment. Given this distribution, we can compute the probability the trial will be successful, allowing for our current uncertainty about how well the treatment works. The general technique of eliciting a probability distribution from experts is used in Bayesian statistics (to obtain a 'prior' distribution) and in probabilistic risk analysis. The challenge in expert elicitation is how to convert domain-specific knowledge and uncertainty to a probability distribution, in particular when we need a distribution for some parameter in a statistical model that the experts may find difficult to assess directly. Other problems include what to do when there are different experts who disagree with each other, and how to justify the final choice of probability distribution.There have only been a small number of publications on assurance methods, and these all assume very simple trial designs and analysis, for example, that there will be a single treatment group, a single control group, and that the data will be analyses with a solitary two-sample t-test. The aim of this project is to develop assurance methods for more complex trial designs such as adaptive designs. The student will first conduct a literature review of clinical trial designs and existing assurance methods, and in consultation with the industrial partner, choose some clinical trial designs for the development of assurance methods. The type of data to be collected within a particular trial design will be specified, and the statistical model used to analyse the data will be determined. The uncertain parameters in the model will then be identified. For the chosen design, the deliverables will be1. an elicitation protocol, setting out what questions to ask the (treatment-specific) experts, such that probability distributions can be constructed for all the uncertain parameters in the model.2. specification of computational methods for calculating assurances.3. adaptation of the current design protocol, to exploit the elicited assurance.4. web-based apps to implement the methods, produced with the R package shiny.
在设计临床试验时,必须仔细计划试验招募的患者数量。如果数量太少,试验就有可能无法提供足够的证据证明治疗有效。如果患者人数过多,一些患者将被不必要地纳入研究,可能会接受当时可能被证明无效的治疗。传统上,选择患者人数时,使用“把握度”的统计概念。非正式地说,临床试验的功效是试验成功并证明治疗有效的概率。关键的是,权力假设新的治疗方法真的像预期的那样有效。但在研究开始之前,我们无法知道这是真的。在实践中,试验成功的概率远低于功效计算所预测的概率,这是非常昂贵的,本项目将开发一种替代统计方法:“保证”,在试验进行之前适当评估治疗效果的不确定性,以便对试验成功的概率做出更现实的评估。具体而言,将为更复杂类型的试验设计开发保证方法。在保证方法中,首先规定试验的格式和分析试验数据的方法,就像传统功效计算一样。然后,我们从专家那里“引出”一个概率分布--由现有的证据和专业知识支持--来代表新治疗方法有效性的不确定性。有了这个分布,我们就可以计算出试验成功的概率,考虑到我们目前对治疗效果的不确定性。从专家那里获得概率分布的一般技术用于贝叶斯统计(以获得“先验”分布)和概率风险分析。专家启发的挑战是如何将特定领域的知识和不确定性转换为概率分布,特别是当我们需要统计模型中某些参数的分布时,专家可能会发现难以直接评估。其他问题包括当不同的专家意见不一致时该怎么办,以及如何证明概率分布的最终选择是合理的。关于保证方法的出版物只有少数,这些都假设非常简单的试验设计和分析,例如,将有一个单一的治疗组,一个单一的对照组,并且将使用单独双样本t检验分析数据。该项目的目的是为更复杂的试验设计(如适应性设计)开发保证方法。学生将首先对临床试验设计和现有的保证方法进行文献综述,并与工业合作伙伴协商,选择一些临床试验设计用于保证方法的开发。将规定在特定试验设计中收集的数据类型,并确定用于分析数据的统计模型。模型中的不确定参数将被识别。对于所选的设计,可交付成果将是1。一个启发协议,列出了什么问题问(治疗特定)专家,这样就可以为模型中的所有不确定参数构建概率分布。计算保证的计算方法规范。适应当前的设计协议,以利用引出的保证。基于web的app实现方法,用R包制作的shiny。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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其他文献
Internet-administered, low-intensity cognitive behavioral therapy for parents of children treated for cancer: A feasibility trial (ENGAGE).
针对癌症儿童父母的互联网管理、低强度认知行为疗法:可行性试验 (ENGAGE)。
- DOI:
10.1002/cam4.5377 - 发表时间:
2023-03 - 期刊:
- 影响因子:4
- 作者:
- 通讯作者:
Differences in child and adolescent exposure to unhealthy food and beverage advertising on television in a self-regulatory environment.
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- DOI:
10.1186/s12889-023-15027-w - 发表时间:
2023-03-23 - 期刊:
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The association between rheumatoid arthritis and reduced estimated cardiorespiratory fitness is mediated by physical symptoms and negative emotions: a cross-sectional study.
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- DOI:
10.1007/s10067-023-06584-x - 发表时间:
2023-07 - 期刊:
- 影响因子:3.4
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ElasticBLAST: accelerating sequence search via cloud computing.
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10.1186/s12859-023-05245-9 - 发表时间:
2023-03-26 - 期刊:
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Amplified EQCM-D detection of extracellular vesicles using 2D gold nanostructured arrays fabricated by block copolymer self-assembly.
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- DOI:
10.1039/d2nh00424k - 发表时间:
2023-03-27 - 期刊:
- 影响因子:9.7
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的其他文献
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