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

“荟萃分析”是一种统计方法，用于结合多项研究的结果，研究同一临床问题，例如治疗是否有效。荟萃分析的目的是将所有研究的结果联合收割机，以产生结果的总体总结（例如，治疗是否有效），这有助于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策。要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，它们通常来自招募数百名患者并随访数年的临床试验。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，以充分利用它。然而，不幸的是，在目前的实践中，大多数研究人员忽略了包含在可用的荟萃分析数据中的有价值的信息。这是因为许多医疗保健研究不仅仅关注结果，例如疾病复发时间（称为“无病生存期”）和死亡时间（称为“总生存期”），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，患者的疾病复发时间通常与他们的死亡时间相关，因为当疾病复发时，死亡时间通常会在不久之后。因此，无病生存率和总生存率结果是相关的，但通过独立地对每个结果进行荟萃分析，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生存期结果，但未报告无病生存期结果，则该研究仍可通过识别该结局与可用总生存期信息的相关性来提供无病生存期信息。然而，目前大多数研究者在对无病生存结果进行荟萃分析时，只是简单地“扔掉”了这项研究，从而浪费了可用的数据。我们认为，更好的方法是对相关结果进行荟萃分析，并利用其相关信息，以充分利用可用数据。做到这一点的方法是“多元荟萃分析”，这是一种统计方法，用于同时荟萃分析所有结果，同时识别和解释它们的相关性。这使得荟萃分析的结果，比方说，结果A，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的研究中获得证据。由于这种方法使用了更多的可用信息，它可能会导致更可靠的结果为医生和医疗专业人员。我们提出的研究项目旨在促进，并鼓励更广泛地使用，多元荟萃分析在当前的实践。我们将首先通过科克伦妊娠和分娩组的综述，对多变量荟萃分析的使用如何改变现有的临床结论进行实证研究，以促进为什么该方法是重要的，并根据更多的信息更新妊娠和分娩的临床结论。然后，我们将开发统计措施，“标志”时，多变量荟萃分析是有益的，所以直接通知研究人员时，他们应该使用多变量的方法。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需要的，但往往是缺失的。最后，我们将开发多变量方法来荟萃分析多种不良结局。目前的方法处理更常见的结果，但不良结果是罕见的，因此需要更合适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。

'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.