MICA: Model Based Network Meta-Analysis for Pharmacometrics and Drug-Development

MICA：基于模型的药理学和药物开发网络荟萃分析

• 批准号: MR/M005615/1
• 负责人: Nicky Welton
• 金额: $ 25.05万
• 依托单位: University of Bristol
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2014
• 资助国家: 英国
• 起止时间: 2014 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FM005615%2F1
• 关键词: MICA; Model; Based; Network; Meta

In the development of new drugs, studies are conducted to compare the relative benefits of the drug at different doses with placebo and/or other active drugs (which may also be at different doses). Furthermore, the health outcomes may be measured repeatedly over time. In order to decide whether to take the new drug forward into larger clinical trials, the results from all studies that have been conducted on a new drug are combined in meta-analysis to obtain a pooled estimate of the effect of the drug against placebo or active comparator drugs. Recently methods have been developed to allow for relative benefits to depend on dose and time of measurement in meta-analysis that compares the new drug with placebo (or another drug). However, there may be more than one comparator drug, and they have been measured at various different doses and times. Network meta-analysis is a technique that allows one to compare the relative benefits of multiple drugs that have been compared in randomised clinical trials, where not all drugs have been included in every study. This study aims to combine models of the relationships for the relative health benefits with dose and time, with network meta-analysis. This will allow us to combine information from studies comparing different drugs at different doses and different times, even though those studies may not have included the same dose and times. Decisions as to which drugs to take forward into clinical trials, has substantial impact on all patients. Drug companies have limited resources, and so the decision to invest in one promising drug may come at the expense of another. It is therefore important to make drug-development decisions based on as much available evidence as possible. The methods developed in this project will allow as much existing evidence from comparative studies as possible to contribute to drug development decisions. Furthermore, we will explore the possibility of also incorporating evidence from studies that only sudy a single drug, or studies that compare drugs that we are not directly interested in, but that could help us understand the form of the relationships over dose and time.The methods we will develop may require some strong assumptions. It is therefore very important to check whether those assumptions hold, and a key part of this work will be to look at methods to check assumptions and to check how well the models developed fit to the observed data. Decisions should be based on the most robust model predictions, and sensitivity to any assumptions explored. This project will be a collaboration with project partner Pfizer, who will provide the datasets and expertise in dose and time course modelling. The University of Bristol team brings expertise in network meta-analysis, assessing model fit and consistency, and statistical computing. The collaboration is designed to ensure that the methods developed will be relevant to the needs of drug-development organisations, and the interaction with Pfizer will allow the methods to be used by that organisation, and publications and disemination plans will introduce the methods more widely. This approach will help the methods be used by industry to better invest their resources into drugs to improve patient health based on a better summary of the available evidence.

在新药物的开发中，进行研究以比较不同剂量的药物与安慰剂和/或其他活性药物（也可能是不同剂量）的相对益处。此外，健康结果可以在一段时间内反复测量。为了决定是否将新药进行更大规模的临床试验，将所有对新药进行的研究结果结合在一起进行荟萃分析，以获得该药物对安慰剂或活性比较药物的综合估计。最近开发的方法允许在比较新药与安慰剂（或其他药物）的荟萃分析中相对获益取决于剂量和测量时间。然而，可能有一种以上的比较药物，它们已经以各种不同的剂量和时间进行了测量。网络荟萃分析是一种技术，它允许人们比较在随机临床试验中比较的多种药物的相对益处，在随机临床试验中，并非所有药物都被纳入每项研究。本研究旨在结合剂量和时间对相对健康益处的关系模型，并结合网络元分析。这将使我们能够从比较不同剂量和不同时间的不同药物的研究中获得信息，即使这些研究可能没有包括相同的剂量和时间。决定将哪些药物用于临床试验，对所有患者都有重大影响。制药公司的资源有限，因此决定投资一种有前景的药物可能会以牺牲另一种药物为代价。因此，重要的是根据尽可能多的现有证据作出药物开发决定。本项目开发的方法将允许尽可能多的来自比较研究的现有证据为药物开发决策做出贡献。此外，我们还将探索将研究单一药物的证据纳入研究的可能性，或者将我们不直接感兴趣的药物进行比较的研究，但这可以帮助我们理解剂量和时间之间的关系。我们将要开发的方法可能需要一些强有力的假设。因此，检查这些假设是否成立是非常重要的，而这项工作的一个关键部分将是研究检查假设的方法，以及检查所开发的模型与观测数据的拟合程度。决策应该基于最可靠的模型预测，以及对所探索的任何假设的敏感性。该项目将与项目合作伙伴辉瑞公司合作，辉瑞公司将提供剂量和时间过程建模方面的数据集和专业知识。布里斯托尔大学的团队带来了网络元分析、评估模型拟合和一致性以及统计计算方面的专业知识。合作的目的是确保所开发的方法与药物开发组织的需求相关，与辉瑞的互动将允许该组织使用这些方法，出版物和传播计划将更广泛地介绍这些方法。这种方法将有助于工业界利用这些方法更好地将资源投入到药物上，从而根据对现有证据的更好总结来改善患者的健康。

项目成果

Network Meta-Analysis for Decision-Making

用于决策的网络元分析

• 发表时间: 2018
• 作者: Dias Sofia

A threshold analysis assessed the credibility of conclusions from network meta-analysis.

• DOI: 10.1016/j.jclinepi.2016.07.003
• 发表时间: 2016-12
• 期刊: JOURNAL OF CLINICAL EPIDEMIOLOGY
• 影响因子: 7.2
• 作者: Caldwell, Deborah M.;Ades, A. B.;Dias, Sofia;Watkins, Sarah;Li, Tianjing;Taske, Nichole;Naidoo, Bhash;Welton, Nicky J.
• 通讯作者: Welton, Nicky J.

Drugs to reduce bleeding and transfusion in adults undergoing cardiac surgery: a systematic review and network meta-analysis

减少接受心脏手术的成人出血和输血的药物：系统评价和网络荟萃分析

• DOI: 10.1002/14651858.cd013427
• 发表时间: 2019
• 期刊: Cochrane Database of Systematic Reviews
• 影响因子: 8.4
• 作者: Beverly A

Assessing the consistency assumptions underlying network meta-regression using aggregate data.

• DOI: 10.1002/jrsm.1327
• 发表时间: 2019-06
• 期刊: Research synthesis methods
• 影响因子: 9.8
• 作者: Donegan S;Dias S;Welton NJ
• 通讯作者: Welton NJ

Effective dose 50 method as the minimal clinically important difference: Evidence from depression trials.

• DOI: 10.1016/j.jclinepi.2021.04.002
• 发表时间: 2021-09
• 期刊: Journal of clinical epidemiology
• 影响因子: 7.2
• 作者: Bauer-Staeb C;Kounali DZ;Welton NJ;Griffith E;Wiles NJ;Lewis G;Faraway JJ;Button KS
• 通讯作者: Button KS