Exemplos de uso de Poisson regression with robust variance em Inglês e suas traduções para o Português
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Rates by Poisson regression with robust variance were analyzed.
One is the log-binomial model and other is the poisson regression with robust variance.
Poisson regression with robust variance was performed using the software stata 11.2.
Multivariate analysis was performed through Poisson regression with robust variance.
Poisson regression with robust variance was applied for verification of variables associated with high scores.
Adjusted prevalence ratios were obtained by poisson regression with robust variance p 0.05.
Poisson regression with robust variance was used in the crude and adjusted analyses, taking the cluster sampling into account.
In the analysis, the chi-square and poisson regression with robust variance were applied.
Poisson regression with robust variance was used to analyze nine distinct outcomes number of new cases.
For the multivariate model, it was used poisson regression with robust variance and a hierarchical model was performed.
Crude prevalence ratios for all independent variables were then calculated by means of poisson regression with robust variance.
This result was confirmed in the analysis of Poisson regression with robust variance and crude and adjusted PR Table 4.
Poisson regression with robust variance was calculated in order to identify the prevalence ratio of hypocholesterolemia and liver transplantation.
To examine the factors related to the phase angle was used poisson regression with robust variance.
In the statistical analysis,we used poisson regression with robust variance to estimate crude and adjusted prevalence ratios.
To investigate the association between regular access and adherence to the medications of the CEAF,we used Poisson regression with robust variance using different analysis models.
In the adjusted analysis, Poisson regression with robust variance was used and the results were expressed as prevalence ratios.
The utilized statistical model was generalized linear regression with binomial family andlogarithmic link, and Poisson regression with robust variance when there was no convergence.
Prevalence ratios were estimated(pr) by poisson regression with robust variance hierarchical, based on the theoretical model of andersen.
Poisson regression with robust variance estimation was used in the multivariate model, variables that presented statistical significance of 20% were inserted p.
The inferential analysis was performed using Poisson regression with robust variance and Spearman's correlation.
Finally, Poisson regression with robust variance was used to estimate the association prevalence ratios between independent variables and the status alive/dead when discharged from the FCV.
The Chi-square test was used for the crude analysis and Poisson regression with robust variance for the adjusted analysis.
In the adjusted analysis, Poisson regression with robust variance was used, respecting the hierarchy among possible factors associated with the outcome.
MMR trends in the state andmacro-regions were analyzed through Poisson regression with robust variance using SPSS program, version 19.0.
The multivariate analyses used Poisson regression with robust variance, with results expressed as prevalence ratios PR and their respective 95%CI.
The associations with the outcomes were measured through prevalence ratios obtained by poisson regression with robust variance, with 95% confidence interval.
For the hierarchical multivariable analysis, Poisson regression with robust variance was used, as the prevalence of the outcome was high.
The association between clinical variables and30-day mortality was established by Poisson regression with robust variance, which was due to the high prevalence of the outcome analyzed> 10.
We performed crude andadjusted analyses by Poisson regression with robust variance for estimating prevalence rates and related 95% confidence intervals of 95% 95% CI.