Effect size: a statistical basis for clinical practice
DOI:
https://doi.org/10.15448/1980-6523.2018.1.29437Keywords:
effect size, P value, statistical interpretation, clinical decision-making, clinical effectiveness.Abstract
OBJECTIVE: Effect size (ES) is the statistical measure which quantifies the strength of a phenomenon and is commonly applied to observational and interventional studies. The aim of this review was to describe the conceptual basis of this measure, including its application, calculation and interpretation.
RESULTS: As well as being used to detect the magnitude of the difference between groups, to verify the strength of association between predictor and outcome variables, to calculate sample size and power, ES is also used in meta-analysis. ES formulas can be divided into these categories: I – Difference between groups, II – Strength of association, III – Risk estimation, and IV – Multivariate data. The d value was originally considered small (0.20 > d ≤ 0.49), medium (0.50 > d≤ 0.79) or large (d ≥ 0.80); however, these cut-off limits are not consensual and could be contextualized according to a specific field of knowledge. In general, a larger score implies that a larger difference was detected.
CONCLUSION: The ES report, in conjunction with the confidence interval and P value, aims to strengthen interpretation and prevent the misinterpretation of data, and thus leads to clinical decisions being based on scientific evidence studies.
References
Cohen J. Statistical power analysis for the behavioral sciences. 2th ed. Ney Jersey, NJ: Lawrence Eribaum; 1988.
Sullivan GM, Feinn R. Using effect size – or why the p value is not enough. J Grad Med Educ 2012;4:279-282.
https://doi.org/10.4300/JGME-D-12-00156.1
Altman DG, Bland JM. Absence of evidence is not evidence of absence. BMJ 1995;311:485.
https://doi.org/10.1136/bmj.311.7003.485
Cohen J. The earth is round (p<.05). Ame Psychol 1994;49:997-1003.
https://doi.org/10.1037/0003-066X.49.12.997
Johnson D. The insignificance of statistical significance testing. J Wildl Manage 1999;63:763-72.
https://doi.org/10.2307/3802789
Wasserstein RL, Lazar NA. The ASA’s statement on p-values: context, process, and purpose. Am Stat 2016;70:129-33.
https://doi.org/10.1080/00031305.2016.1154108
Ferguson CJ. An (ES) primer: A guide for clinicians and researchers. Prof Psychol Res 2009;40:532-8.
https://doi.org/10.1037/a0015808
Kirk RE. Practical significance: A concept whose time has come. Educ Psychol Meas1996;56:746-59.
https://doi.org/10.1177/0013164496056005002
Pandis N. The effect size. Am J Orthod Dentofacial Orthop 2012;142: 739-40.
https://doi.org/10.1016/j.ajodo.2012.06.011
Espírito-Santo H, Daniel F. Calculating and reporting effect sizes on scientific papers (1): p < 0.05 limitations in the analysis of mean differences of two groups. RPICS 2015;1:3-16.
Khalilzadeh J, Tasci ADA. Large sample size, significance level, and the effect size: Solutions to perils of using big data for academic research. Tour Manag 2017;62:89-96.
https://doi.org/10.1016/j.tourman.2017.03.026
Ferrari R, Caram LMO, Garcia T, Paiva SAR, Vale AS, Tanni SE. Effect size. Pneumol Paul 2016;29:73-4.
Fritz CO, Morris PE, Richler JJ. (ES) estimates: current use, calculations, and interpretation. J Exp Psychol Gen 2012;141:2-18.
https://doi.org/10.1037/a0024338
Nakagawa S, Cuthill IC. effect size, confidence interval and statistical significance: a practical guide for biologists. Biol Rev Camb Philos Soc 2007;82:591-605.
https://doi.org/10.1111/j.1469-185X.2007.00027.x
Hulley SB, Cummings SR, Browner WS, Grady DG, Newman TB. Delineando a pesquisa clínica. 4th ed. Porto Alegre: Artmed; 2015.
Bakker M, Dijk AV, Wicherts JM. The rules of the game called psychological science. Perspect Psychol Sci 2012;7:543-54.
https://doi.org/10.1177/1745691612459060
Anderson SF, Kelley K, Maxwell SE. Sample-size planning for more accurate statistical power: A method adjusting sample effect sizes for publication bias and uncertainty. Psychol Sci 2017;28:1547-62.
https://doi.org/10.1177/0956797617723724
Bradley MT, Brand A. Alpha values as a function of sample size, effect size, and power: accuracy over inference. Psychol Rep 2013;112:835-44.
https://doi.org/10.2466/03.49.PR0.112.3.835-844
Tomczak M, Tomczak E. The need to report effect size estimates revisited. An overview of some recommended measures of effect size. TSS 2014; 1:19-25.
Greenland S, Senn SJ, Rothman KJ, Carlin JB, Poole C, Goodman SN, et al. Statistical tests, p values, confidence intervals, and power: a guide to misinterpretations. Eur J Epidemiol 2016;31 337-50.
https://doi.org/10.1007/s10654-016-0149-3
Hedges LV. Distributional theory for Glass´s estimator of effect size and related estimators. J Educ Behav Stat 1981;6:107-28.
https://doi.org/10.3102/10769986006002107
Glass GV. Primary, secondary, and meta-analysis of research. Educ Res 1976;5:3-8.
https://doi.org/10.3102/0013189X005010003
Lalongo C. Understanding the effect size and its measures. Biochem Med 2016;26:150-63.
https://doi.org/10.11613/BM.2016.015
Levine TR, Hullett CR. Eta squared, partial eta squared, and misreporting of effect size in communication research. Hum Commun Res 2002;28: 612-25.
https://doi.org/10.1111/j.1468-2958.2002.tb00828.x
Bakeman R. Recommended effect size statistics for repeated measures designs. Behav Res Methods 2005;37:379-84.
https://doi.org/10.3758/BF03192707
Espírito-Santo H, Daniel F. Calculating and reporting effect sizes on scientific papers (2): Guide to report the strength of relationships. RPICS 2017;1:53-64.
Maher JM, Markey, CM, Ebert-May D. The other half of the story: effect size analysis in quantitative research. CBE-Life Sci Educ 2013;12:345-51.
https://doi.org/10.1187/cbe.13-04-0082
Selya AS, Rose JS, Dierker LC, Hedeker D, Mermelstein RJ. A practical guide to calculation Cohen’s f2, a measure of local effect size, from PROC MIXED. Front Psychol 2012;3:1-6.
https://doi.org/10.3389/fpsyg.2012.00111
McHugh ML. The odds ratio: calculation, usage, and interpretation. Biochem Med 2009;19:120-6.
https://doi.org/10.11613/BM.2009.011
Téllez A, García CH, Corral-Verdugo V. Effect size, confidence intervals and statistical power in psychological research. Psychol Russia 2015;8:27-47.
https://doi.org/10.11621/pir.2015.0303
Nyirongo VB, Mukaka MM, Kalilani-Phiri LV. Statistical pitfalls in medical research. Malawi Med J 2008;20:15-18.
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