Meta-analyses are becoming increasingly popular, especially in the fields of cardiovascular disease prevention and treatment. They are often considered to be a reliable source of evidence for making healthcare decisions. Unfortunately, problems among meta-analyses such as the misapplication and misinterpretation of statistical methods and tests are long-standing and widespread. The purposes of this statement are to review key steps in the development of a meta-analysis and to provide recommendations that will be useful for carrying out meta-analyses and for readers and journal editors, who must interpret the findings and gauge methodological quality. To make the statement practical and accessible, detailed descriptions of statistical methods have been omitted. Based on a survey of cardiovascular meta-analyses, published literature on methodology, expert consultation, and consensus among the writing group, key recommendations are provided. Recommendations reinforce several current practices, including protocol registration; comprehensive search strategies; methods for data extraction and abstraction; methods for identifying, measuring, and dealing with heterogeneity; and statistical methods for pooling results. Other practices should be discontinued, including the use of levels of evidence and evidence hierarchies to gauge the value and impact of different study designs (including meta-analyses) and the use of structured tools to assess the quality of studies to be included in a meta-analysis. We also recommend choosing a pooling model for conventional meta-analyses (fixed effect or random effects) on the basis of clinical and methodological similarities among studies to be included, rather than the results of a test for statistical heterogeneity.

荟萃分析越来越受欢迎，尤其是在心血管疾病的预防和治疗领域。通常认为它们是做出医疗保健决定的可靠证据来源。不幸的是，荟萃分析中的问题，如对统计方法和检验的错误应用和误解，是长期存在且普遍存在的。本声明的目的是回顾进行荟萃分析的关键步骤，并提供一些建议，这些建议对于进行荟萃分析以及必须解释研究结果并评估方法学质量的读者和期刊编辑是有用的。为了使该声明实用且易于访问，省略了对统计方法的详细描述。根据对心血管荟萃分析的调查，有关方法学的已发表文献，专家咨询，并在写作小组之间达成​​共识，并提供了重要建议。建议加强了几种当前的做法，包括协议注册；全面的搜索策略；数据提取和抽象的方法；识别，测量和处理异质性的方法；以及汇总结果的统计方法。应停止使用其他实践，包括使用证据水平和证据层次结构来评估不同研究设计（包括荟萃分析）的价值和影响，以及使用结构化工具评估纳入荟萃研究的质量-分析。我们还建议根据纳入研究的临床和方法学相似性，为常规荟萃分析（固定效应或随机效应）选择合并模型，