Multivariate meta-analysis of multiple correlated outcomes: development and application of methods, with empirical investigation of clinical impact

多个相关结果的多变量荟萃分析：方法的开发和应用，以及临床影响的实证研究

• 批准号: MR/J013595/1
• 负责人: Richard Riley
• 金额: $ 43.46万
• 依托单位: University of Birmingham
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2013
• 资助国家: 英国
• 起止时间: 2013 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FJ013595%2F1
• 关键词: Multivariate; meta; analysis; multiple; correlated

'Meta-analysis' is the statistical approach for combining results from multiple studies examining the same clinical question, such as whether a treatment is effective or not. The aim of a meta-analysis is to combine the results from all studies, to produce an overall summary of the findings (e.g. whether the treatment is effective) which helps doctors and healthcare professionals make patient-related decisions based on all the evidence available. Reliable data to be combined in a meta-analysis are expensive and hard to come by; for example, they often come from clinical trials that have recruited hundreds of patients and followed them up for several years. It is thus important that when researchers undertake a meta-analysis they use all the available data in the most efficient way, to get the most of it. Yet, unfortunately in current practice most researchers are ignoring valuable information that is contained in the available meta-analysis data. This is because many healthcare studies have more than outcome of interest, such as time to recurrence of disease (called 'disease-free survival') and time to death (called 'overall survival'), and researchers currently meta-analyse each outcome separately. However, such multiple outcomes are often related to each other, i.e. they are correlated. For example, a patient's time to recurrence of disease is generally associated with their time of death, as when disease returns the time to death will often follow shortly after. So disease-free survival and overall survival results are correlated, but by meta-analysing each outcome independently, researcher ignores this correlation and thus lose potentially valuable information. In particular, if a study reports overall survival results, but not disease-free survival results, this study can still provide information about disease-free survival by recognising the correlation this outcome has with the available overall survival information. Yet at the moment most researchers simply 'throw away' this study when meta-analysing the disease-free survival results, and thus waste available data.We believe that a better approach is to meta-analyse correlated outcomes together and utilise their correlated information to get the most out of the available data. The way to do this is a 'multivariate meta-analysis', which is a statistical approach for meta-analysing all the outcomes simultaneously whilst recognising and accounting for their correlation. This allows meta-analysis results for, say, outcome A to be informed by evidence from studies reporting outcome A and also from studies reporting other correlated outcomes B, C and D. As this approach uses more of the information available, it can potentially lead to more reliable results for doctors and healthcare professionals.Our proposed research project aims to facilitate, and encourage more widespread use of, multivariate meta-analysis in current practice. We will firstly perform an empirical investigation as to how the use of multivariate meta-analysis would change existing clinical conclusions with Reviews of the Cochrane Pregnancy and Childbirth Group, to promote why the approach is important and to update clinical conclusions for pregnancy and childbirth based on more information. We will then develop statistical measures that 'flag' when multivariate meta-analysis is beneficial, so to directly inform researchers when they should use the multivariate approach. We will then develop and extend methods to estimating the correlation between outcomes, which are needed to apply the multivariate approach but are often missing. Finally, we will develop multivariate methods for meta-analysing multiple adverse outcomes. Current methods deal with more common outcomes, but adverse outcomes are rare and so more appropriate methods are needed for such situations. We will also produce a multivariate meta-analysis website, suitable statistical software, and a training course for researchers.

“荟萃分析”是一种统计方法，它将多项研究的结果结合在一起，考察同一临床问题，如治疗是否有效。荟萃分析的目的是将所有研究的结果结合起来，产生研究结果的总体总结(例如，治疗是否有效)，以帮助医生和医疗保健专业人员根据所有可用的证据做出与患者相关的决定。在荟萃分析中结合可靠的数据是昂贵的，而且很难获得；例如，这些数据通常来自招募了数百名患者并对他们进行了数年跟踪的临床试验。因此，重要的是，当研究人员进行荟萃分析时，他们以最有效的方式使用所有可用的数据，以获得最大限度的数据。然而，不幸的是，在目前的实践中，大多数研究人员忽略了可用荟萃分析数据中包含的有价值的信息。这是因为许多医疗保健研究不仅仅是关注结果，比如疾病复发时间(称为无病生存时间)和死亡时间(称为总体生存时间)，而研究人员目前分别对每个结果进行荟萃分析。然而，这种多重结果往往是相互关联的，即它们是相互关联的。例如，患者复发的时间通常与他们的死亡时间有关，因为当疾病复发时，死亡时间通常会紧随其后。因此，无病生存率和总体生存率是相关的，但通过对每个结果进行单独的荟萃分析，研究人员忽略了这种相关性，从而丢失了潜在的有价值的信息。特别是，如果一项研究报告了总体生存结果，但不是无病生存结果，这项研究仍然可以通过认识到这一结果与现有的总体生存信息的相关性来提供关于无病生存的信息。然而，目前大多数研究人员在荟萃分析无病生存结果时只是“抛弃”了这项研究，从而浪费了可用的数据。我们认为，更好的方法是将相关的结果汇总分析，并利用它们的相关信息来最大限度地利用可用的数据。要做到这一点，方法是“多变量元分析”，这是一种统计方法，用于同时对所有结果进行元分析，同时识别和解释它们的相关性。这使得，比方说，结果A的荟萃分析结果可以从报告结果A的研究以及报告其他相关结果B、C和D的研究的证据中获得信息。由于这种方法使用了更多可用的信息，它可能会为医生和医疗保健专业人员带来更可靠的结果。我们建议的研究项目旨在促进并鼓励在当前实践中更广泛地使用多变量元分析。我们将首先进行一项关于多变量荟萃分析的使用将如何改变Cochrane妊娠和分娩组综述的现有临床结论的实证调查，以宣传该方法为什么重要，并根据更多信息更新妊娠和分娩的临床结论。然后，我们将开发当多变量Meta分析有益时的统计指标，以便直接通知研究人员何时应该使用多变量方法。然后，我们将开发和扩展方法来估计结果之间的相关性，这是应用多变量方法所必需的，但经常被遗漏。最后，我们将开发多元方法来对多种不良结果进行荟萃分析。目前的方法处理更常见的结果，但不良结果很少见，因此需要更适当的方法来处理这种情况。我们还将制作一个多元荟萃分析网站，合适的统计软件，并为研究人员提供培训课程。

项目成果

Meta-analysis using individual participant data: one-stage and two-stage approaches, and why they may differ.

• DOI: 10.1002/sim.7141
• 发表时间: 2017-02-28
• 期刊: Statistics in medicine
• 影响因子: 2
• 作者: Burke DL;Ensor J;Riley RD
• 通讯作者: Riley RD

Inference for correlated effect sizes using multiple univariate meta-analyses.

• DOI: 10.1002/sim.6789
• 发表时间: 2016-04-30
• 期刊: Statistics in medicine
• 影响因子: 2
• 作者: Chen Y;Cai Y;Hong C;Jackson D
• 通讯作者: Jackson D

Bayesian meta-analytical methods to incorporate multiple surrogate endpoints in drug development process.

• DOI: 10.1002/sim.6776
• 发表时间: 2016-03-30
• 期刊: Statistics in medicine
• 影响因子: 2
• 作者: Bujkiewicz S;Thompson JR;Riley RD;Abrams KR
• 通讯作者: Abrams KR

The role of secondary outcomes in multivariate meta-analysis.

• DOI: 10.1111/rssc.12274
• 发表时间: 2018-11
• 期刊: Journal of the Royal Statistical Society. Series C, Applied statistics
• 作者: Copas JB;Jackson D;White IR;Riley RD
• 通讯作者: Riley RD

Bayesian bivariate meta-analysis of correlated effects: Impact of the prior distributions on the between-study correlation, borrowing of strength, and joint inferences.

• DOI: 10.1177/0962280216631361
• 发表时间: 2018-03
• 期刊: Statistical methods in medical research
• 影响因子: 2.3
• 作者: Burke DL;Bujkiewicz S;Riley RD
• 通讯作者: Riley RD