Sarkar Soumya, Baidya Dalim Kumar
Department of Anaesthesiology, AIIMS, Kalyani, West Bengal, India.
Department of Anaesthesiology, Critical Care and Pain Medicine, AIIMS, Guwahati, Assam, India.
Indian J Anaesth. 2025 Jan;69(1):147-152. doi: 10.4103/ija.ija_1155_24. Epub 2025 Jan 11.
A forest plot is a graphical tool to visualise and interpret the summary of estimated results in a meta-analysis. However, it is limited by its inability to control for random error, publication bias, heterogeneity, and confounding factors. Therefore, the interpretation can be misleading, resulting in flawed conclusions. A careful interpretation and other complementing techniques are necessary to comprehensively summarise evidence in meta-analyses, reducing the risk of erroneous conclusions. The present review explores the components of forest plots, how to interpret them correctly, fundamental limitations, and techniques to mitigate them, along with examples to provide practical insights to ensure more accurate and reliable meta-analytic results.
森林图是一种用于可视化和解释荟萃分析中估计结果总结的图形工具。然而，它存在局限性，无法控制随机误差、发表偏倚、异质性和混杂因素。因此，其解释可能会产生误导，导致结论有缺陷。在荟萃分析中，需要进行仔细的解释并采用其他补充技术来全面总结证据，以降低得出错误结论的风险。本综述探讨了森林图的组成部分、如何正确解释它们、基本局限性以及减轻这些局限性的技术，并通过实例提供实际见解，以确保获得更准确可靠的荟萃分析结果。
