Basel Institute for Clinical Epidemiology and Biostatistics, University Hospital Basel, Switzerland.
Swiss Med Wkly. 2012 Mar 9;142:w13518. doi: 10.4414/smw.2012.13518. eCollection 2012.
Meta-analyses overcome the limitation of small sample sizes or rare outcomes by pooling results from a number of individual studies to generate a single best estimate. As long as a meta-analysis is not limited by poor quality of included trials, unexplainable heterogeneity and/or reporting bias of individual trials, meta-analyses can be instrumental in reliably demonstrating benefit or harm of an intervention when results of individual randomised controlled trials are conflicting or inconclusive. Therefore meta-analyses should be conducted as part of a systematic review, i.e., a systematic approach to answer a focused clinical question. Important features of a systematic review are a comprehensive, reproducible search for primary studies, selection of studies using clear and transparent eligibility criteria, standardised critical appraisal of studies for quality, and investigation of heterogeneity among included studies. Cumulative meta-analysis may prevent delays in the introduction of effective treatments and may allow for early detection of harmful effects of interventions. As opposed to meta-analysis based on aggregate study data, individual patient data meta-analyses offer the advantage to use standardised criteria across trials and reliably investigate subgroup effects of interventions. Network meta-analysis allows the integration of data from direct and indirect comparisons in order to compare multiple treatments in a comprehensive analysis and determine the best treatment among several options. We conclude that meta-analysis has become a popular, versatile, and powerful tool. If rigorously conducted as part of a systematic review, it is essential for evidence-based decision making in clinical practice as well as on the health policy level.
荟萃分析通过汇总多项研究的结果，生成一个单一的最佳估计值，克服了小样本量或罕见结果的局限性。只要荟萃分析不受纳入试验质量差、个体试验无法解释的异质性和/或报告偏倚的限制，当个体随机对照试验的结果相互矛盾或不确定时，荟萃分析就可以可靠地证明干预措施的益处或危害。因此，荟萃分析应作为系统评价的一部分进行，即系统地回答一个有针对性的临床问题的方法。系统评价的重要特征包括全面、可重复地搜索原始研究，使用明确透明的纳入标准选择研究，对研究进行标准化的质量评估，并调查纳入研究之间的异质性。累积荟萃分析可以防止有效治疗方法的引入延迟，并可能及早发现干预措施的有害影响。与基于汇总研究数据的荟萃分析相比，个体患者数据荟萃分析具有在试验之间使用标准化标准并可靠地研究干预措施亚组效应的优势。网络荟萃分析允许整合直接和间接比较的数据，以便在综合分析中比较多种治疗方法，并确定多种选择中最佳的治疗方法。我们得出结论，荟萃分析已成为一种流行、多功能且强大的工具。如果作为系统评价的一部分进行严格的荟萃分析，它对于临床实践和卫生政策层面的循证决策至关重要。
