Abstract
Informing in the studies about the effect size of an intervention or the impact of the factor(s) about an outcome, allows better decision-making for the application of the results in clinical practice. This article presents different methods to analyze the effect size, which can be through direct or indirect statistical methods. Within the direct methods, there’s the difference in means between groups and the difference of absolute or relative frequencies. Among the indirect methods, Cohen's “d” family (which are based on standard deviation values), the “r and R2” family, measures of association (e.g. OR, RR, HR), and impact measures (e.g. NNT) are shown. The decision to use any of these methods depends on the objectives of the study and the measuring scale that is used to assess the results, as well as the data distribution. In order to enhance the understanding of the methods described in this article, examples are included, and the need to include level of precision (e.g. confidence intervals) is highlighted, along with the clinical decision thresholds for a better interpretation.
References
Cohen HW. P values: use and misuse in medical literature. Am J Hypertens. 2011;24(1):18-23. DOI: 10.1038/ajh.2010.205
Sullivan GM, Feinn R. Using effect size-or why the p value is not enough. J Grad Med Educ. 2012;4(3):279-282. DOI: 10.4300/JGME-D-12-00156.1
Kelley K, Preacher KJ. On effect size. Psychol Methods. 2012;17(2):137-152. DOI: 10.1037/a0028086
Alhija FN-A, Levy A. Effect size reporting practices in published articles. Educ Psychol Measurment. 2009;69(2):245-265. DOI: 10.1177/0013164408315266
Grissom RJ, Kim JJ. Effect. sizes for reseach: univariate and multivariate application. Segunda edición. Inlgaterra: Taylor & Francis Group; 2012.
Ventura-León J. Otras formas de entender la d de Cohen. Rev Evaluar. 2018;18(3):73-78. DOI: 10.35670/1667-4545.v18.n3.22305
Lakens D. Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs. Front Psychol. 2013;4:863. DOI: 10.3389/fpsyg.2013.00863
Martínez-Ezquerro JD, Riojas-Garza A, Rendón-Macías ME. Significancia clínica sobre significancia estadística. Cómo interpretar los intervalos de confianza a 95. Rev Alerg Mex. 2017;64(4):477-486. DOI: 10.29262/ram.v64i4.334
Nakagawa S, Cuthill IC. Effect size, confidence interval and statistical significance: a practical guide for biologists. Biol Rev Camb Philos Soc. 2007;82(4):591-605. DOI: 10.1111/j.1469-185X.2007.00027.x
Silva-Ayçaguer LC, Suárez-Gil P, Fernández-Somoano A. The null hypothesis significance test in health sciences research (1995-2006): statistical analysis and interpretation. BMC Med Res Methodol. 2010;10:44. Disponible en: https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/1471-2288-10-44
O’Brien SF, Yi QL. How do I interpret a confidence interval? Transfusion. 2016;56(7):1680-1683. DOI: 10.1111/trf.13635
Schulz R, O’Brien A, Czaja S, Ory M, Norris R, Martire LM, et al. Dementia caregiver intervention research: in search of clinical significance. Gerontologist. 2002;42(5):589-602. DOI: 10.1093/geront/42.5.589
Schechtman E. Odds ratio, relative risk, absolute risk reduction, and the number needed to treat--which of these should we use? Value Health. 2002;5(5):431-436. DOI: 10.1046/J.1524-4733.2002.55150.x
Kraemer HC, Kupfer DJ. Size of treatment effects and their importance to clinical research and practice. Biol Psychiatry. 2006;59(11):990-996. DOI: 10.1016/j.biopsych.2005.09.014
Rendón-Macías ME, García H, Villasís-Keever M. Medidas de riesgo, asociación e impacto en los estudios de investigación clínica. Cómo interpretarlas para su aplicación en la atención médica. Rev Alerg Mex. 2021;68(1):65-75.
Peterson SJ, Foley S. Clinician’s guide to understanding effect size, alpha level, power, and sample size. Nutr Clin Pract. 2021;36(3):598-605. DOI: 10.1002/ncp.10674
Pek J, Flora DB. Reporting effect sizes in original psychological research: a discussion and tutorial. Psychol Methods. 2018;23(2):208-225. DOI: 10.1037/met0000126
Maher JM, Markey JC, Ebert-May D. The other half of the story: effect size analysis in quantitative research. CBE Life Sci Educ. 2013;12(3):345-351. DOI: 10.1187/cbe.13-04-0082
Selya AS, Rose JS, Dierker LC, Hedeker D, Mermelstein RJ. A practical guide to calculating Cohen’s f2, a measure of local effect size, from PROC MIXED. Front Psychol. 2012;3:111. DOI: 10.3389/fpsyg.2012.00111
Cohen J. A power primer. Psychol Bull. 1992;112(1):155-159. DOI: 10.1037//0033-2909.112.1.155
Ialongo C. Understanding the effect size and its measures. Biochem Med (Zagreb). 2016;26(2):150-163. DOI: 10.11613/BM.2016.015
Grzybowski A, Mianowany M. Statistics in ophthalmology revisited: the (effect) size matters. Acta Ophthalmol. 2018;96(7):e885-e888. DOI: 10.1111/aos.13756.
Lininger M, Riemann BL. Statistical primer for athletic trainers: using confidence intervals and effect sizes to evaluate clinical meaningfulness. J Athl Train. 2016;51(12):1045-1048. DOI: 10.4085/1062-6050-51.12.14
Calin-Jageman RJ. The new statistics for neuroscience majors: thinking in effect sizes. J Undergrad Neurosci Educ. 2018;16(2):E21-E25.
Bender R. Calculating confidence intervals for the number needed to treat. Control Clin Trials. 2001;22(2):102-110. DOI: 10.1016/s0197-2456(00)00134-3
Kelter R. Analysis of Bayesian posterior significance and effect size indices for the two-sample t-test to support reproducible medical research. BMC Med Res Methodol. 2020;20(1):88. Disponible en: https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-020-00968-2
Kelter R. Simulation data for the analysis of Bayesian posterior significance and effect size indices for the two-sample t-test to support reproducible medical research. BMC Res Notes. 2020;13(1):452. DOI: 10.1186/s13104-020-05291-z
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