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Applications of proportional odds ordinal logistic regression models and continuation ratio models in examining the association of physical inactivity with erectile dysfunction among type 2 diabetic patients

  • Mathew, Anil C. (Department of Community Medicine, PSG Institute of Medical Sciences and Research) ;
  • Siby, Elbin (Department of Biostatistics, St. Thomas College) ;
  • Tom, Amal (Department of Biostatistics, St. Thomas College) ;
  • Kumar R, Senthil (Department of Endocrinology, PSG Institute of Medical Sciences and Research)
  • 투고 : 2020.12.15
  • 심사 : 2021.03.14
  • 발행 : 2021.03.31

초록

[Purpose] Many studies have observed a high prevalence of erectile dysfunction among individuals performing physical activity in less leisure-time. However, this relationship in patients with type 2 diabetic patients is not well studied. In exposure outcome studies with ordinal outcome variables, investigators often try to make the outcome variable dichotomous and lose information by collapsing categories. Several statistical models have been developed to make full use of all information in ordinal response data, but they have not been widely used in public health research. In this paper, we discuss the application of two statistical models to determine the association of physical inactivity with erectile dysfunction among patients with type 2 diabetes. [Methods] A total of 204 married men aged 20-60 years with a diagnosis of type 2 diabetes at the outpatient unit of the Department of Endocrinology at PSG hospitals during the months of May and June 2019 were studied. We examined the association between physical inactivity and erectile dysfunction using proportional odds ordinal logistic regression models and continuation ratio models. [Results] The proportional odds model revealed that patients with diabetes who perform leisure time physical activity for over 40 minutes per day have reduced odds of erectile dysfunction (odds ratio=0.38) across the severity categories of erectile dysfunction after adjusting for age and duration of diabetes. [Conclusion] The present study suggests that physical inactivity has a negative impact on erectile function. We observed that the simple logistic regression model had only 75% efficiency compared to the proportional odds model used here; hence, more valid estimates were obtained here.

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참고문헌

  1. Armstrong BG, Sloan M. Ordinal regression models for epidemiologic data. Am J Epidemiol. 1989;129:191-204. https://doi.org/10.1093/oxfordjournals.aje.a115109
  2. Cox DR, Snell EJ. Analysis of binary data. Biometrics. 1989;46:550.
  3. Selvin E, Burnett AL, Platz EA. Prevalence and risk factors for erectile dysfunction in the US. Am J Med. 2007;120:151-7. https://doi.org/10.1016/j.amjmed.2006.06.010
  4. Bacon CG, Mittleman MA, Kawachi I, Giovannucci E, Glasser DB, Rimm EB. A prospective study of risk factors for erectile dysfunction. J Urol. 2006;176:217-21. https://doi.org/10.1016/S0022-5347(06)00589-1
  5. Ponholzer A, Temml C, Mock K, Marszalek M, Obermayr R, Madersbacher S. Prevalence and risk factors for erectile dysfunction in 2869 men using a validated questionnaire. Eur Urol. 2005;47:80-6. https://doi.org/10.1016/j.eururo.2004.08.017
  6. Senthil KR, Arjun VS, Anandhu KJS, Sarath M, Darshan M, Yunsheng M, Anil CM. Socio-demographic and clinical correlates of erectile dysfunction among men with type 2 diabetes mellitus-a cross sectional study in South India. Indian J Diabetes Endocrinol. 2020;2:17-22.
  7. Walker SH, Duncan DB. Estimation of the probability of an event as a function of several independent variables. Biometrika. 1967;54:167-79. https://doi.org/10.1093/biomet/54.1-2.167
  8. McCullagh P. Regression models for ordinal data. J R StatSoc Series B Stat Methodol. 1980;42:109-27.
  9. Sperandei S. Understanding logistic regression analysis. Biochem Med. 2014;24:12-8. https://doi.org/10.11613/BM.2014.003
  10. Fienberg SE. The analysis of cross-classified categorical data. Massachusetts Institute of Technology Press: Cambridge and London. 1980.
  11. Greenwood C, Farewell V. A comparison of regression models for ordinal data in an analysis of transplanted-kidney function. Can J Stat. 1988;16:325-35. https://doi.org/10.2307/3314931
  12. Ananth CV, Kleinbaum DG. Regression models for ordinal responses: a review of methods and applications. Int J epidemiol. 1997;26:1323-33. https://doi.org/10.1093/ije/26.6.1323
  13. Cox DR. Regression models and life-tables. J R StatSoc Series B Stat Methodol. 1972;34:187-202.
  14. McCullagh P. Regression models for ordinal data. J R StatSoc Series B Stat Methodol. 1980;42:109-27.
  15. Allison P. Logit and loglinear analysis using the SAS system. University of Pennsylvania. 1998.
  16. Cox C. Multinomial regression models based on continuation ratios. Stat Med. 1988;7:435-41. https://doi.org/10.1002/sim.4780070309
  17. Mcgowan, Melissa J. MJ: ordinal outcomes with the continuation ratio model. Proceedings of the Northeast SAS Users Group Conference. 2000.
  18. Cox DR, Hinkley DV. Theoretical statistics. CRC Press. 1979.
  19. Miller TA. Diagnostic evaluation of erectile dysfunction. Am Fam Physician. 2000;61:95-110.
  20. Maresca L, D'Agostino M, Castaldo L, Vitelli A, Mancini M, Torella G, Lucci R, Albano G, Del Forno D, Ferro M, Altieri V, Giallauria F, Vigorito C.Exercise training improves erectile dysfunction (ED) in patients with metabolic syndrome on phosphodiesterase-5 (PDE-5) inhibitors. Monaldi Arch Chest Dis. 2013;80:177-83.
  21. Dorey G, Speakman MJ, Feneley RC, Swinkels A, Dunn CD. Pelvic floor exercises for erectile dysfunction. BJU int. 2005;96:595-7. https://doi.org/10.1111/j.1464-410X.2005.05690.x