The Anatomy of the Forest Plot graphic

Main Article Content

Dariana Rojas-Jiménez https://orcid.org/0009-0007-5488-0786
Alma Itzel Castañeda-Aca https://orcid.org/0009-0002-1532-5863
Juan José Alonzo-Martínez https://orcid.org/0009-0005-3390-1635
Marlene López-Sánchez https://orcid.org/0000-0002-1996-7209
Ivonne Analí Roy-García https://orcid.org/0000-0002-1859-3866
Rodolfo Rivas-Ruiz https://orcid.org/0000-0002-5967-7222

Keywords

Meta-Analysis, Statistics, Risk, Forest Plot

Abstract

Graphs used in scientific articles help improve the understanding of results. One of the most widely used graphs in recent scientific literature is the forest plot. Its growing popularity is related to its versatility. Although it was initially employed to present results of meta-analyses, it is now used to display findings from individual studies, for both qualitative and quantitative variables, as long as a confidence interval —most commonly the 95% CI— can be calculated. This graph not only allows the presentation of results from univariate analyses but also from multivariable analyses, making it applicable to diverse fields of scientific knowledge. In this article, we present, in addition to the history of the forest plot, a description of its components —its anatomy— and a tutorial on how to create one using statistical software.

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References

1. Lewis S. Forest plots: trying to see the wood and the trees. BMJ 2001;322(7300):1479-80. doi: 10.1136/bmj.322.7300.1479

2. Miquel Porta. A DICTIONARY OF EPIDEMIOLOGY. Sixth Edition. New York: Oxford University Press; 2014.

3. Rivas-Ruiz R, Castelán-Martínez OD, Pérez-Rodríguez M, et al. Investigación clínica XXIII. Del juicio clínico a los metaanálisis. Rev Med Inst Mex Seguro Soc 2014;52(5):558-65.

4. Rivas-Ruiz R, Silva-Jivaja KM, Galicia-Solórzano JL, et al. Pertinencia de los modelos multivariantes en medicina de urgencias. REIE 2021;3(2):6574. doi: 10.24875/REIE.21000048.

5. Freiman JA, Chalmers TC, Smith H, et al. The Importance of Beta, the Type II Error and Sample Size in the Design and Interpretation of the Randomized Control Trial: Survey of 71 Negative Trials. N Engl J Me 1978;299(13):690-4. doi: 10.1056/NEJM197809282991304.

6. Wang W, Lu S, Xie T. Optimal confidence intervals for the relative risk and odds ratio. Statistics in Medicine 2023;42(3):281-96. doi: 10.1002/sim.9617.

7. Sharma PK, Yadav M. Confidence Interval: Advantages, Disadvantages and the Dilemma ofInterpretation. RRCT 2024;19(1):76-80. doi: 10.2174/0115748871266250231120043345.

8. Sarkar S, Baidya DK. Meta-analysis - interpretation of forest plots: A wood for the trees. Indian Journal of Anaesthesia 2025;69(1):147-52. doi: 10.4103/ija.ija_1155_24.

9. Kandany VN, Gómez Muñoz HM, Marte MI. Metanálisis, una revisión sistemática cuantitativa: conceptos básicos. RAM 2025;13(2):113-20. doi: 10.61222/2cj0by11

10. Lewis J, Ellis S. A statistical appraisal of post-infarction beta-blocker trials. Prim Cardiol 1982;(suppl 1):31-7. doi: 10.1056/NEJM199207233270406.

11. Antiplatelet Trialists' Collaboration. Secondary prevention of vascular disease by prolonged antiplatelet treatment. Antiplatelet Trialists' Collaboration. Br Med J (Clin Res Ed). 1988;296(6618):320-31.

12. Ferreira-Hermosillo A, Roy-García I, Rivas-Ruiz R, et al. Progresión de talla y peso en niños y niñas entre 6 y 12 años y su diferencia con las tablas de Ramos Galván 40 años después. GMM 2020;156(2):3197. doi: 10.24875/GMM.19005463.

13. Moreno-Noguez M, Rivas-Ruiz R, Roy-García IA, Pacheco-Rosas DO, et al. Risk factors associated with SARS-CoV-2 pneumonia in the pediatric population. BMHIM 2021;78(4):5587. doi: 10.24875/BMHIM.20000263.

14. Zhou X, Gong Y. Exploration in association between vitamin D, sleep quality, and osteoarthritis: A modeling study. Medicine 2024;103(40):e40021. doi: 10.1097/MD.0000000000040021.

15. Chuang HY, Wu HM, Chien TW, et al. The use of the time-to-event index (Tevent) to compare the negative impact of COVID-19 on public health among continents/regions in 2020 and 2021: An observational study. Medicine 2022;101(49):e30249. doi: 10.1097/MD.0000000000030249.

16. Yan YH, Chien TW. The use of forest plot to identify article similarity and differences in characteristics between journals using medical subject headings terms: A protocol for bibliometric study. Medicine 2021;100(6):e24610. doi: 10.1097/MD.0000000000024610.

17. Boumans MMA, Aerts W, Pisani L, et al. Diagnostic accuracy of lung ultrasound in diagnosis of ARDS and identification of focal or non-focal ARDS subphenotypes: a systematic review and meta-analysis. Crit Care 2024;28(1):224. doi: 10.1186/s13054-024-04985-1.

18. Su J, Tie X, Zhou R, Zou T, et al. Risk factors and a nomogram model for deep vein thrombosis in critically ill patients with sepsis: a retrospective analysis. Sci Rep 2025;15(1):16641. doi: 10.1038/s41598-025-01660-5.

19. Foster GA, Goldsmith CH. Problems commonly associated with forest plots addressed using high resolution graphics in SAS®. En San Francisco, CA: SAS Institute Inc. (Cary, NC); 2006. p. 139-131. Disponible en: https://support.sas.com/resources/papers/proceedings/proceedings/sugi31/139-31.pdf

20. Villegas-Quintero VE, Rivas-Ruíz R, García-Rivero AA, et al. Eficacia y seguridad de la atorvastatina en eventos cardiovasculares mayores: metaanálisis [Efficacy and safety of atorvastatin in major cardiovascular events: Meta-analysis]. Rev Med Inst Mex Seguro Soc 2023;61(Suppl 3):S407-S415. doi: 10.5281/zenodo.8319748.

21. Nabzo S, Fau C. Metaanálisis: bases conceptuales, análisis e interpretación estadística. Revista Mexicana de Oftalmología 2020;94(6S):260-73. doi: 10.24875/RMO.M20000134.

22. Higgins JPT, Deeks JJ. Chapter 10: Analysing data and undertaking meta-analyses. En: Cochrane Handbook for Systematic Reviews of Interventions Version 64 (updated August 2023) [Internet]. Cochrane; 2023. Disponible en: https://www.cochrane.org/authors/handbooks-and-manuals/handbook/current/chapter-10

23. Cordero CP, Dans AL. Key concepts in clinical epidemiology: detecting and dealing with heterogeneity in meta-analyses. Journal of Clinical Epidemiology 2021;130:149-51. doi: 10.1016/j.jclinepi.2020.09.045.

24. Wasserstein RL, Schirm AL, Lazar NA. Moving to a world beyond “p < 0.05.” Am Stat 2019;73(Suppl 1):1–19. doi: 10.1080/00031305.2019.1583913.

25. Migliavaca CB, Stein C, Colpani V, et al. Meta‐analysis of prevalence: I2 statistic and how to deal with heterogeneity. Research Synthesis Methods 2022;13(3):363-7. doi: 10.1002/jrsm.1547.

26. Andrade C. Understanding the Basics of Meta-Analysis and How to Read a Forest Plot: As Simple as It Gets. J Clin Psychiatry 2020;81(5):20f13698. doi: 10.4088/JCP.20f13698.

27. Carazo-Díaz C, Prieto-Valiente L. Diferencia de Riesgos, Riesgo Relativo y Odds Ratio [Key Measures in Epidemiology: Risk Difference, Relative Risk and Odds Ratio]. Rev Neurol 2025;80(2):33481. doi: 10.31083/RN33481.

28. Sen S, Yildirim I. A tutorial on how to conduct meta-analysis with IBM SPSS Statistics. Psych 2022;4(4):640–667. doi: 10.3390/psych4040049.