Systematic Reviews and Meta-Analysis of Prognosis Studies (REVAMP): development of core methods, reporting guidelines and a methodology handbook

医疗保健研究正处于一个令人兴奋的新阶段。研究人员现在可以获得大量信息，将个人的特征(如年龄、家族史或遗传信息)与健康结果(如死亡、疼痛水平、抑郁评分)联系起来。研究人员正在利用这些信息帮助卫生专业人员和患者了解和预测未来的结果。这种研究被称为预后研究，涵盖四种类型：总体预后(人群中的平均结局风险是多少？)，预后因素(患者的哪些特征有助于了解他们的结局风险？)，预后模型(我们能否使用多个预后因素来预测个人的风险？)，以及治疗效果的预测因素(我们能预测哪些治疗对个别患者最有效吗？)。预测研究结果使卫生专业人员能够更准确地预测，从而进行干预，以改善个人的健康结果。每年发表数千份预测研究报告，其中许多研究结果不明确或相互矛盾。这使得医疗保健专业人员很难知道如何最好地利用这些发现来改善对患者的护理。这激发了对预后研究进行系统评价的需要。这些研究旨在确定关于特定主题的所有现有预测研究，并评估它们的质量，如果合适的话，结合它们的结果(使用荟萃分析)，为医疗保健专业人员提供总体答案。系统评价方法在医学研究的其他领域已经很成熟，但在预测研究中才刚刚出现。在我们的项目中，我们将开发并免费提供可靠的方法来对预测研究进行系统的审查。我们将这一倡议命名为REVERVAMP，代表对预后研究的系统回顾和元分析。我们提出了一系列五个工作项目(WP)，由在预测研究和系统评价方面具有相当专业知识的研究人员提供。在WP1中，我们将制定和分发总体预后审查指南；这些指南旨在提供特定人群(例如那些被诊断为乳腺癌的人)的平均风险摘要。将制作一份循序渐进的指南，涵盖搜索相关研究、数据提取、批判性评估和荟萃分析等方面。清晰的报告对于那些解释、批判性评估和实施预后研究结果是必不可少的。然而，目前还没有系统评价预后研究的报告指南。因此，在WP2中，我们将为预后评估提供三个报告指南(检查表)：分别针对总体预后、预后因素和预后模型。在WP3和WP5中，我们将为研究人员开发新的方法来计算预后研究需要多少个人。目前的研究往往太小，因此可能会产生误导性的结果。我们将使用数学技术来产生一种新的计算样本量的方法，以便更容易地判断(I)现有的预测模型研究是否足够大以适合于目的，以及(Ii)新的预测研究需要多大的规模才能对未来的最新回顾做出有用的贡献。在WP4中，我们将为进行治疗效果预测因素的审查提供循序渐进的指导，特别是当目的是总结治疗效果是否取决于患者的特征(如年龄)时，如何进行批判性评估和荟萃分析。我们项目的发现将在预后审查方法学手册中传播，并通过一个新的英国研究培训课程传播，此外还将在期刊上发表文章，并通过博客和网站上的教程视频传播，包括www.preprosis Research.com。

Healthcare research is in an exciting new phase. Researchers now have access to large amounts of information to link an individual's characteristics (such as age, family history or genetic information) with health outcomes (such as death, pain level, depression score). Researchers are using this information to help health professionals and patients understand and predict future outcomes. Such research is called prognosis research, and covers four types: overall prognosis (what is the average outcome risk in a population?), prognostic factors (what characteristics of patients help understand their outcome risk?), prognostic models (can we predict an individual's risk using multiple prognostic factors?), and predictors of treatment effect (can we predict which treatments work best for individual patients?). Prognosis research findings allow health professionals to more accurately predict and therefore intervene to improve the health outcomes of individuals.Thousands of prognosis studies are published each year, and many have unclear or contradictory findings. This makes it difficult for healthcare professionals to know how best to use these findings to improve the care of their patients. This motivates the need for systematic reviews of prognosis studies. These aim to identify all existing prognosis studies on a particular topic, and to appraise their quality and, if appropriate, combine their results (using meta-analysis) to provide overall answers for healthcare professionals. Systematic review methods are well-established in other areas of medical research, but are only just emerging for prognosis research. In our project, we will develop and make freely available reliable methods to undertake systematic reviews of prognosis studies. We have named this initiative REVAMP, which stands for Systematic Reviews and Meta-Analysis of Prognosis Studies.We propose a series of five work projects (WPs) to be delivered by researchers with considerable expertise in prognosis research and systematic reviews. In WP1, we will develop and disseminate guidance for reviews of overall prognosis; which aim to provide a summary of the average risk in a particular population (e.g. those diagnosed with breast cancer). A step-by-step guide will be produced, covering aspects such as searching for relevant studies, data extraction, critical appraisal, and meta-analysis. Clear reporting is essential for those interpreting, critically appraising, and implementing prognosis research findings. However, no reporting guidelines currently exist for systematic reviews of prognosis studies. Therefore, in WP2 we will provide three reporting guidelines (checklists) for prognosis reviews: one for each of overall prognosis, prognostic factors and prognostic models. These will be developed through a consensus process involving researchers, healthcare professionals, organisations developing clinical guidelines, and patients.In WP3 and 5, we will develop new ways for researchers to calculate how many individuals are needed for a prognosis study. Current studies are often too small and so may have misleading results. We will use mathematical techniques to produce a new way to calculate sample size, so that it will be easier to judge (i) whether an existing prognostic model study was large enough to be fit-for-purpose, and (ii) what size a new prognosis study needs to be to contribute usefully to a future updated review. In WP4 we will produce step-by-step guidance for conducting reviews of predictors of treatment effect, particularly how to do critical appraisal and meta-analysis when the aim is to summarise whether a treatment's effect depends on patient characteristics such as age.Our project's findings will be disseminated in a Methodology Handbook for Prognosis Reviews and through a new UK-based research training course, in addition to publications in journals and via blogs and tutorial videos at websites including www.prognosisresearch.com.