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ARTÍCULO DE REVISIÓN 369 causa una disminución significativa (0,7 mg) en el consumo de ondansetrón utilizado para tratar las náuseas y vómitos postoperatorios. Conclusión Las puntuaciones de propensión son una herramienta estadística que permite manejar el sesgo de confusión, que inevitablemente surgirá en estudios epidemiológicos observacionales y, por lo tanto, posibilitan obtener una identificación de los efectos causales aproximable a la lograda con los Ensayos Clínicos Aleatorizados. Referencias 1. Bakker G, Clark L. La explicación. Una introducción a la filosofía de la ciencia. México: F.C.E. 1994. 2. Höfler M. Causal inference based on counterfactuals. BMC Med Res Methodol 2005; 5: 28-40. 3. Rubin D. Estimating causal effects in randomized and non randomized studies. Journal of educational psychology 1974; 66: 688-701. 4. Coughlin S. Causal inference & scientific paradigms in epidemiology. Bentham Science Publishers 2010. 5. Hernan MA, Robins JM. Causal Inference I. Chapman & Hall CRC 2014. 6. Little R, Rubin D. Causal effects in clinical and epidemiological studies via potential Outcomes: Concepts and analytical approaches. Annu Rev Public Health 2000; 21: 121-45. 7. Neyman J. On the application of probability theory to agricultural experiments. Essay of principles. Section 9. Statistical Science 1923 (1990); 5: 465-80. 8. Crocco G, Farinas Del Cerro R, Herzig A. Conditionals: from Philosophy to Computer Science, Oxford Clarendon Press. 1995. 9. Greenland S, Pearl J, Robins J. Causal diagrams for epidemiologic research. Epidemiology 1999; 10: 37-48. 10. Pearl J. Causality. 2nd edition. NY, USA. Cambridge University Press 2009. 11. Harrel FEJ, Lee KL, Califf RM. Regression modeling strategies for improved prognostic prediction. Stat Med 1984; 3: 143-52. 12. Wilkenmayer WC, Kurth T. Propensity scores: help or hype? Nephrol Dial Transplant 2004; 19: 1672-3. 13. Stuart EA, Rubin DB. Matching methods for causal inference: Designing observational studies. Best Practices in Quantitative Methods. J Osborne Thousand Oaks, Ca: Sage Publishing. 2007. 14. Concato J, Shah N, Horwitz R. Randomized controlled trials, observational studies and the hierarchy of research designs. N Engl J Med 2000; 342: 1878-86. 15. Rothman K, Greenland S, Lash TL. Modern Epidemiology. 3d ed. Philadelphia: Lippincot Williams &Wilkins; 2008. 16. Fletcher RH, Fletcher SW, Wagner EH. Clinical Epidemiology: the essentials, 4th ed. Baltimore, USA: Wilkins & Wilkins, 2007. 17. Rosenbaum P, Rubin. The central role of propensity scores in observational studies for causal effects. Biometrika 1983; 70: 41-55. 18. Pattorno E, Grotta A, Belloco R, Schneeweeiss S. Propensity score methodology for confounding control in health care utilization databases. Epidemiology Biostatistics and Public Health 2013; 10: 8940-3. 19. Becker S, Ichino A. Estimation of average treatment effects based on propensity scores. The Stata journal 2002; 2: 358-77. 20. Glynn R, Schneeweiss S, Stürmer T. Indications for propensity scores and review of their use in pharmacoepidemiology. Basic clin pharmacol toxicol 2006; 98: 253-9. 21. Pirracchio R, Resche-Rigon M, Chevret M. Evaluation of the propensity score methods for estimating marginal Odds ratio in case of small simple size. BMC Medical research methodology 2012; 12: 70-80. 22. Guo SY, Fraser MW. Propensity Score Analysis: Statistical Methods and Applications (Advanced Quantitative Techniques in Social Research). 2014. 23. Stürmer T, Joshi M, Glynn RJ, Avorn J, Rothman KJ, Schneeweiss S. A review of the application of propensity scores methods yielded increasing use, advantages in specific settings, but not substantially different estimates compared with conventional multivariable methods. J Clin Epidemiol 2006; 59: 437-47. 24. Shah BR, Laupacis A, Hux JE, Austin PC. Propensity score methods gave similar results to traditional regression modeling in observational studies: a systematic review. J Clin Epidemiol 2005; 58: 550-9. 25. Pattanayaka C, Rubina D, Zeel E. Métodos de puntuación de propensión para crear una distribución equilibrada de las covariables en los estudios observacionales. Rev Esp Cardiol 2011; 64: 897-903. 26. Cepeda MS, Boston R, Farrar JT, Strom BL. Comparison of logistic regression versus propensity scores when the number of events is low and there are multiple confounders. Am J Epidemiol 2003; 158: 280-7. 27. Arbogast PR, Ray WA. Perfomance of Disease Risk Score, Propensity scores and traditional Multivariable Outcome Regression in the presence of multiple con- Puntuaciones de propensión - D. Ojeda et al Rev Med Chile 2016; 144: 364-370


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