Exemplos de uso de Poisson regression models em Inglês e suas traduções para o Português
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Poisson regression models were used to assess associated factors. RESULTS.
Similarly, the case-crossover methodology validates the results of Poisson regression models.
Six Poisson regression models were developed referring to six dependent variables.
Descriptive analyses were executed in SPSS and the Poisson regression models were executed in Stata software version 12.0.
Poisson regression models were developed to estimate crude PR and PR adjusted for independent variables.
Minutes/week and perceived barriers was analyzed by calculating the prevalence ratio(PR) in Poisson regression models.
We created similar Poisson regression models as reported above for individual countries.
The statistical significance of prevalence ratios obtained in Poisson regression models was assessed by the Wald test.
Poisson regression models with robust variance were used in the crude and in the adjusted analyses.
Variables with p<0.10 were included in Poisson regression models with robust variance to obtain the adjusted PR(adPR) and 95%CI.
Poisson regression models were performed in order to estimate the association of job strain and mental health outcomes.
The statistical significance of the prevalence ratios obtained in the Poisson regression models were evaluated using the Wald test.
Poisson regression models showed good overall adjustment and we did not observe multicollinearity problems or significant interactions.
Following descriptive and crude analyses, Poisson regression models taking the clustering of the sample into account were carried out.
The association between body image andthe conduction of extreme weight-related attitudes was studied by Poisson regression models.
Poisson regression models were used to analyze the relationship between the number of deaths from 2000 to 2006 and the selected explanatory variables.
For the variables of nutritional status/food consumption that were significant in the bivariate analysis, Poisson regression models with robust variance were used.
Poisson regression models with robust estimators stratified by gender were used to obtain the prevalence ratio PR with 95% confidence interval 95%CI.
The descriptive analyses were performed in the SAS software, version 9.1.3(2003, SAS Institute, Cary,NC), and the Poisson regression models were developed in Stata software, version 13.0 StataCorp.
Adjusted and non-adjusted Poisson regression models with robust variance were calculated in order to ascertain the ratios of prevalence of the dependent and independent variables.
The assessment of association used the prevalence ratio(PR),as calculated by Poisson regression models with complex sampling and weighted sample adjustments.
Meanwhile, Poisson regression models have been used as a good alternative for obtaining adequate PR estimates, even in cross-sectional studies, when the outcome is frequent.
We assessed changes in the disease epidemiology in the city for the entire study period and estimated incidence and mortality rates of md(overall and by age group)from 2008 to 2012 using poisson regression models.
Poisson regression models with robust variance and estimation of unadjusted and adjusted prevalence ratios were used to establish associations at a 5% significance level for inclusion in the final model. .
Associations between variables were expressed as crude and adjusted prevalence ratios andtheir respective 95% confidence intervals, using Poisson regression models with robust variance.
We used Poisson regression models with robust variance to estimate the prevalence ratio(PR) of having excess weight(overweight and obese) according to the portion size consumed in each beverage group.
The verification of the epidemiological aspects was done using multiple logistic regression models or poisson regression models, which were selected according to the frequency of occurrence of ectoparasites.
Even though Poisson regression models are usually used for counting variables, Barros and Hirakata demonstrated the use of such models as an alternative to logistic regression for cross-sectional studies with dichotomous outcomes.
The class of generalized linear models comprises the models of conventional multiple linear regression, as well as the Poisson regression models, negative binomial models and logistics, among others.
Descriptive analyses were executed in SPSS and the Poisson regression models were executed in Stata software version 12.0 Stata Corp LP, College Station, TX, USA, using the appropriate set of svy commands for the analysis of complex samples and ensuring the necessary weighting, considering the sample design.