Exemplos de uso de Poisson regression model was used em Inglês e suas traduções para o Português
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A Poisson regression model was used to identify interactions and confounding factors for DD and ARI.
The generalized additive Poisson regression model was used as the outcome is a discrete, quantitative variable.
Poisson regression model was used for estimating crude and adjusted prevalence ratios, their related 95% confidence intervals and p-values Wald test.
In the assessment of temporal trends the Poisson regression model was used, in which the dependent variable was the number of deaths and the centralized calendar year was the explanatory variable.
A Poisson regression model was used to estimate adjusted and unadjusted prevalence ratios and 95% confidence intervals 95%CI, considering the sample design effect using the Stata svy command.
For multivariate analysis, Poisson Regression model was used, being expressed by the Prevalence Ratio PR and its respective confidence intervals CI95.
The Poisson regression model was used to identify factors associated with hypertriglyceridemic waist.
For the multivariate analysis, a robust Poisson regression model was used and the variables that presented a p-value less than 0.20 were tested, with those that presented p-values less than 0.05, with 95% CI, remaining in the final model. .
The Poisson regression model was used to estimate the crude and adjusted prevalence ratios PR, and the 95%CI.
The Poisson regression model was used to assess the relationship between year of diagnosis and incidence of AIDS, adjusted by sex.
The Poisson regression model was used, with robust variance estimation, maintaining variables with p value lower than 0.20 in the model. .
The poisson regression model was used to identify factors associated with ms to estimate the prevalence ratio(pr) and its 95% confidence interval ci.
Subsequently, the Poisson regression model was used to conduct a multivariate assessment of associations between the variable response or outcome and independent variables to obtain the adjusted prevalence.
Poisson regression model was used to assess associations between the dependent and independent variables, considering an outcome of 50%, confidence interval of 95% and a non-response rate of 30.
The Poisson regression model was used to estimate the crude and adjusted prevalence ratios PR and 95% confidence intervals 95%. When assessing the statistical significance of differences between groups, the significance level of 5% was considering.
Poisson regression models were used to assess associated factors. RESULTS.
Poisson regression models were used to obtain PR of PAOD by cardiovascular risk factors.
Poisson regression models were used to analyze the relationship between the number of deaths from 2000 to 2006 and the selected explanatory variables.
Poisson regression models were used separately to investigate association between sufficient vitamin d status(outcome variable) and being enough active(> 300 minutes per week) or presenting high screen time> 2hours per d.
To identify factors associated with fall prevention practices, the Poisson multiple regression model was used, with robust variance and the stepwise forward method.
The final adjustment of the multivariate Poisson regression model was performed using the goodness-of-fit test.
Bivariate and multivariate Poisson Robust Regression models were used to obtain crude Prevalence Ratios cPR and adjusted Prevalence Ratios aPR and their respective 95% Confidence Intervals 95%CI.
The Poisson regression model is generally used in epidemiology to analyze longitudinal studies in which the response is the number of episodes of an event occurring over a given time.