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Torman VB, Camey SA. Bayesian models as a unified approach to estimate relative risk (or prevalence ratio) in binary and polytomous outcomes. Emerg Themes Epidemiol. 2015;12:8doi: 10.1186/s12982-015-0030-y.
Torman, V. B., & Camey, S. A. (2015). Bayesian models as a unified approach to estimate relative risk (or prevalence ratio) in binary and polytomous outcomes. Emerging themes in epidemiology, 128. https://doi.org/10.1186/s12982-015-0030-y
Torman, Vanessa Bielefeldt Leotti, and Camey, Suzi Alves. "Bayesian models as a unified approach to estimate relative risk (or prevalence ratio) in binary and polytomous outcomes." Emerging themes in epidemiology vol. 12 (2015): 8. doi: https://doi.org/10.1186/s12982-015-0030-y
Torman VB, Camey SA. Bayesian models as a unified approach to estimate relative risk (or prevalence ratio) in binary and polytomous outcomes. Emerg Themes Epidemiol. 2015 Jun 20;12:8. doi: 10.1186/s12982-015-0030-y. eCollection 2015. PMID: 26097494; PMCID: PMC4473845.
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Emerg Themes Epidemiol. 2015 Jun 20;12:8. doi: 10.1186/s12982-015-0030-y. eCollection 2015.

Bayesian models as a unified approach to estimate relative risk (or prevalence ratio) in binary and polytomous outcomes.

Emerging themes in epidemiology

Vanessa Bielefeldt Leotti Torman, Suzi Alves Camey

Affiliations

  1. Department of Statistics, Federal University of Rio Grande do Sul (UFRGS), Porto Alegre, RS Brazil ; Post-Graduate Program in Epidemiology, Federal University of Rio Grande do Sul (UFRGS), Porto Alegre, RS Brazil.

PMID: 26097494 PMCID: PMC4473845 DOI: 10.1186/s12982-015-0030-y

Abstract

BACKGROUND: Disadvantages have already been pointed out on the use of odds ratio (OR) as a measure of association for designs such as cohort and cross sectional studies, for which relative risk (RR) or prevalence ratio (PR) are preferable. The model that directly estimates RR or PR and correctly specifies the distribution of the outcome as binomial is the log-binomial model, however, convergence problems occur very often. Robust Poisson regression also estimates these measures but it can produce probabilities greater than 1.

RESULTS: In this paper, the use of Bayesian approach to solve the problem of convergence of the log-binomial model is illustrated. Furthermore, the method is extended to incorporate dependent data, as in cluster clinical trials and studies with multilevel design, and also to analyse polytomous outcomes. Comparisons between methods are made by analysing four data sets.

CONCLUSIONS: In all cases analysed, it was observed that Bayesian methods are capable of estimating the measures of interest, always within the correct parametric space of probabilities.

Keywords: Bayesian models; Common outcomes; Dependent data; Polytomous outcomes; Prevalence ratio; Relative risk

References

  1. Cad Saude Publica. 2014 Jan;30(1):21-9 - PubMed
  2. Annu Rev Public Health. 2001;22:167-87 - PubMed
  3. Int J Epidemiol. 1994 Feb;23(1):201-3 - PubMed
  4. BMJ. 1998 Jan 3;316(7124):54 - PubMed
  5. Int J Epidemiol. 1997 Feb;26(1):220-3 - PubMed
  6. Am J Epidemiol. 2003 May 15;157(10):940-3 - PubMed
  7. Biostatistics. 2005 Jan;6(1):39-44 - PubMed
  8. JAMA. 1998 Nov 18;280(19):1690-1 - PubMed
  9. BMJ. 1998 Oct 24;317(7166):1155-6; author reply 1156-7 - PubMed
  10. Occup Environ Med. 1998 Apr;55(4):272-7 - PubMed
  11. Int J Public Health. 2008;53(3):165-7 - PubMed
  12. Stat Med. 2009 Nov 10;28(25):3049-67 - PubMed
  13. Epidemiology. 2010 Nov;21(6):855-62 - PubMed
  14. BMC Med Res Methodol. 2003 Oct 20;3:21 - PubMed
  15. Am J Epidemiol. 2004 Apr 1;159(7):702-6 - PubMed
  16. Biom J. 2007 Dec;49(6):889-902 - PubMed
  17. Int J Epidemiol. 1995 Oct;24(5):1064-7 - PubMed
  18. Obstet Gynecol. 2008 Feb;111(2 Pt 1):423-6 - PubMed
  19. Am J Epidemiol. 1986 Jan;123(1):174-84 - PubMed
  20. Clin Trials. 2011 Feb;8(1):48-58 - PubMed
  21. Stat Methods Med Res. 2013 Dec;22(6):661-70 - PubMed
  22. Emerg Themes Epidemiol. 2013 Dec 13;10(1):14 - PubMed
  23. Am J Epidemiol. 1987 May;125(5):761-8 - PubMed
  24. Biom J. 2006 Feb;48(1):5-22 - PubMed
  25. J Clin Epidemiol. 1994 Aug;47(8):881-9 - PubMed

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