2019, Número 2
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Rev Mex Pediatr 2019; 86 (2)
Estadística bayesiana: un nuevo paradigma para incorporar en la investigación clínica
Rendón-Macías ME
Idioma: Español
Referencias bibliográficas: 27
Paginas: 43-46
Archivo PDF: 238.14 Kb.
FRAGMENTO
En las últimas décadas, con el propósito de mejorar la inferencia estadística, en el mundo se está retomando el análisis bayesiano. Si bien, estamos conscientes de que pasará tiempo para su aceptación e incorporación, muy probablemente no sustituirá a la estadística clásica, es decir, la estadística frecuentista. De esta forma, consideramos importante que los investigadores y lectores de la literatura médico-científica la conozcan para que puedan comprenderla mejor.
En general, la estadística busca simplificar o resumir información de una investigación para entender,analizar e inferir conclusiones útiles al tomar decisiones. Sus dos objetivos principales son la estimación de parámetros (ej. medias, proporciones, porcentajes, tasa, etc.) y ejecución de pruebas de significancia estadística de la hipótesis nula (PSEHN).
REFERENCIAS (EN ESTE ARTÍCULO)
Hackenberger BK. Bayes or not Bayes, is this the question? Croat Med J. 2019; 60: 50-52.
Curran-Everett D. Exploration in statistics: hypothesis test and P values. Adv Physiol Educ. 2009; 33: 81-86.
Greenland S, Senn SJ, Rothman KJ, Carlin JB, Poole Ch, Goodman SN et al. Statistical test, p values, confidence intervals, and power: a guide to misinterpretations. Eur J Epidemiol. 2016; 31: 337-350.
Goodman SB. Aligning statistical and scientific reasoning. Misunderstanding and misuse of statistical significance impede science. Science. 2016; 352(6290): 1180-1181. doi: 10.1126/science.af5406.
Dienes Z, Mclatichie N. Four reasons to prefer Bayesian analyses over significance testing. Psychon Bull Rev. 2017. doi: 10.3758/s13423-017-1266-z
Martínez-Ezquerro JD, Riojas-Garza A. Rendón-Macías ME. Clinical significance vs statistical significance. How to interpret the confidence interval at 95%. Rev Alerg Mex. 2017; 64(2): 220-227.
Morey RD, Hoekstra R, Rouder JN, Lee MD, Wagenmakers EJ. The fallacy of placing confidence in confidence intervals. Psychon Bull Rev. 2016; 23: 103-123. doi: 10.3758/s13423-015-0947-8.
Greenland S, Poole CH. Living with P values. Resurrecting a Bayesian perspective on frequentist statistics. Epidemiology. 2013; 34: 62-68.
Zangiacomi-Martínez E, Achcar JA. Trends in epidemiology in the 21st century: time to adopt Bayesian methods. Sau Pub Rio Janeiro. 2014; 30(4): 703-714. doi: 10.1590/0102-311X00144013.
Asby D. Bayesian statistics in medicine: a 25 years review. Stat Med. 2006; 25: 3589-3631.
Austin PC, Brunner LJ, Hux JE. Bayes watch: an overview of Bayesian statistics. J Eval Clin Pract. 2002; 8(2): 277-286.
Gurrin LC, Kurinczuck JJ, Burton PR. Bayesian statistics in medical research: an intuitive alternative to conventional data analysis. J Eval Clin Pract. 2000; 6(2): 193-204.
Bittl JA, He Y. Bayesian analysis. A practical approach to interpret clinical trials and clinical practice guidelines. Circ Cardiovasc Qual Outcomes. 20017; 10.e003563. doi: 10.1161/CIRCOUTCOMES.117.003536.
Etz A, Gronau QF, Dablander F, Edelsbrunner PA, Baribault B. How to become a Bayesian in eight easy steps: an annotated reading list. Psychon Bull Rev. 2017; 28. doi: 10.3758/s13423-017-1317-5.
McCulloch TJ. Bayesian statistics: how to quantify uncertainty. Anaest Inten Care. 2011; 39: 1001-1003.
Haskins R, Osmotherly PG, Tuyl F, Rivett DA. Uncertainty in clinical prediction rules: the value of credible intervals. J Orthop & Sport Therp. 2014; 44(2): 85-91.
Wagenmakers EJ, Love J, Marsma M et al. Bayesian inference for psychology. Part II: Example applications with JASP. Psychon Bull Rev. 2017. doi: 10.3758/s13423-017-1323-7.
Matthews RA. Beyond “significance”: principles and practice of the analysis of credibility. R Soc open Sci. 2017; 5: 171047. doi: 10.1098/rsos.171047.
Kruschke JK, Liddell TM. The Bayesian new statistics: hypothesis testing, estimation, meta-analysis, and power analysis from Bayesian perspective. Psychon Bull Rev. 2017. doi: 10.3758/s13423-016-1221-4.
Shin JJ, Zurakowski D. Null hipotheses, interval estimation, and Bayesian analysis. Otolaryngology-Head Neck Surg. 2017; 157(6): 919-920.
Rendón-Macías ME, Riojas-Garza A, Contreras-Estrada D, Martínez-Ezquerro JD. Bayesian analysis. Basic and practical concepts for interpretation and use. Rev Alerg Mex. 2018; 65(3): 205-218.
Weiss RE. Bayesian methods for data analysis. Am J Ophthalmol. 2010; 149(2): 187. doi: 10.1016/j.ajo.2009.11.011.
Jeon M, De Boeck P. Decision qualities of Bayes factor and p value-based hypothesis testing. Am Psych Meth. 2017; 22(2): 340-360.
Hoekstra R, Moden R, van Ravenzwaaij D, Wagenmarkers EJ. Bayesian reanalysis of null results reported in medicine; Strong yet variable evidence for the absence of treatment effects. PLoS ONE. 2018; 13(4): e0195474. doi: 10.1371/journal.pone.0195474.
Wilcox RR, Serang S. Hypothesis testing, p values, confidential intervals, measures of effect size, and bayesian methods in light of modern robust techniques. Educ & Psychol Measement. 2017; 77(4): 673-689.
Quintana DS, Williams DR. Bayesian alternatives for common null-hypothesis tests in psychiatry: a non-technical guide using JASP. BMC Psychiatriy. 2018; 18: 178. doi: 10.1186/s12888-1761-4.
López-Puga J, Krzywinski M, Altman N. Bayes theorem. Incorporate new evidence to update prior information. Nat Meth. 2015; 12(4): 277-278.