BACKGROUND: The most commonly used measures of association in cross-sectional studies are the odds ratio (OR) and the prevalence ratio (PR). Some cross-sectional epidemiologic studies describe their results as OR but use the definition of PR. The main aim of this study was to describe and compare different calculation methods for PR described in literature using two situations (prevalence < 20% and prevalence > 20%). MATERIAL AND METHODS: A literature search was carried out to determine the most commonly used techniques for estimating the PR. The four most frequent methods were: 1) obtaining the OR using non-conditional logistic regression but using the correct definition; 2) using Breslow-Cox regression; 3) using a generalized linear model with logarithmic transformation and binomial family, and 4) using the conversion formula from OR into PR. The models found were replicated for both situations (prevalence less than 20% and greater than 20%) using real data from the 1994 Catalan Health Interview Survey. RESULTS: When prevalence was low, no substantial differences were observed in either the estimators or standard errors obtained using the four procedures. When prevalence was high, differences were found between estimators and confidence intervals although all the measures maintained statistical significance. CONCLUSION: All the methods have advantages and disadvantages. Individual researchers should decide which technique is the most appropriate for their data and should be consistent when using an estimator and interpreting it.
BACKGROUND: The most commonly used measures of association in cross-sectional studies are the odds ratio (OR) and the prevalence ratio (PR). Some cross-sectional epidemiologic studies describe their results as OR but use the definition of PR. The main aim of this study was to describe and compare different calculation methods for PR described in literature using two situations (prevalence < 20% and prevalence > 20%). MATERIAL AND METHODS: A literature search was carried out to determine the most commonly used techniques for estimating the PR. The four most frequent methods were: 1) obtaining the OR using non-conditional logistic regression but using the correct definition; 2) using Breslow-Cox regression; 3) using a generalized linear model with logarithmic transformation and binomial family, and 4) using the conversion formula from OR into PR. The models found were replicated for both situations (prevalence less than 20% and greater than 20%) using real data from the 1994 Catalan Health Interview Survey. RESULTS: When prevalence was low, no substantial differences were observed in either the estimators or standard errors obtained using the four procedures. When prevalence was high, differences were found between estimators and confidence intervals although all the measures maintained statistical significance. CONCLUSION: All the methods have advantages and disadvantages. Individual researchers should decide which technique is the most appropriate for their data and should be consistent when using an estimator and interpreting it.
Authors: Alexandra Tijerina; Yamile Barrera; Elizabeth Solis-Pérez; Rogelio Salas; José L Jasso; Verónica López; Erik Ramírez; Rosario Pastor; Josep A Tur; Cristina Bouzas Journal: Nutrients Date: 2022-06-22 Impact factor: 6.706
Authors: M Bradley Drummond; Gregory D Kirk; Jacquie Astemborski; Meredith C McCormack; Mariah M Marshall; Shruti H Mehta; Robert A Wise; Christian A Merlo Journal: Int J Chron Obstruct Pulmon Dis Date: 2011-01-19
Authors: Juan Carlos Bazo-Alvarez; Frank Peralta-Alvarez; Antonio Bernabé-Ortiz; Germán F Alvarado; J Jaime Miranda Journal: BMC Psychol Date: 2016-05-04