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

在新药开发中，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，健康结果可以随时间重复测量。为了决定是否将新药推进到更大规模的临床试验中，将对新药进行的所有研究的结果合并到荟萃分析中，以获得药物对安慰剂或活性对照药物的效果的汇总估计。最近的方法已经开发出来，允许相对效益取决于剂量和时间的测量在荟萃分析，比较新药与安慰剂（或另一种药物）。然而，可能有一种以上的对照药物，并且已经在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，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，其中并非所有药物都被纳入每项研究。本研究旨在将相对健康效益与剂量和时间关系的联合收割机模型与网络荟萃分析相结合。这将使我们能够联合收割机的信息，从研究比较不同的药物在不同的剂量和不同的时间，即使这些研究可能没有包括相同的剂量和时间。决定哪些药物进入临床试验，对所有患者都有重大影响。制药公司的资源有限，因此投资一种有前途的药物的决定可能会以牺牲另一种药物为代价。因此，根据尽可能多的可用证据做出药物开发决策非常重要。本项目中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，我们还将探索是否有可能纳入仅研究单一药物的研究证据，或比较我们不直接感兴趣的药物的研究证据，但这些研究可以帮助我们了解剂量和时间关系的形式。因此，检查这些假设是否成立是非常重要的，这项工作的一个关键部分将是研究检查假设的方法，并检查开发的模型与观测数据的拟合程度。决策应基于最稳健的模型预测，以及对任何假设的敏感性。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。布里斯托大学的团队带来了网络元分析、评估模型拟合度和一致性以及统计计算方面的专业知识。合作旨在确保开发的方法与药物开发组织的需求相关，与辉瑞的互动将使该组织能够使用这些方法，出版物和淘汰计划将更广泛地介绍这些方法。这种方法将有助于行业使用这些方法，以便更好地将其资源投入药物，从而在更好地总结现有证据的基础上改善患者健康。

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.