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Instead, country of birth was coded for inclusion in the regression as 1) born in Mexico or 2) born elsewhere.
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Our selection of the risk factors for inclusion in the regression model was based on the clinically important factors as well as the results of a univariate analysis.
Fourth, key independent variables for inclusion in the regression models (smoking status, family history of hypertension, diet, exercise) were not collected as part of the study.
A P value of 0.05 was required for inclusion in the regression analysis.
Table 4 shows variables for inclusion in the regression analysis with a p-value below 5%.
Epidemiological and clinical significance of the variables was considered for inclusion in the regression model.
Measures related to quality of life were assessed for inclusion in the regression models.
Collinearity between variables was taken into consideration when selecting variables for inclusion in the regression models.
The independent variables considered for inclusion in the regression model are listed in the first column of Table 1.
Potential confounders considered for inclusion in the regression models were age, sex, urinary creatinine, albumin, and body mass index (BMI).
Simple linear regression and a P value < 0.200 were used to select candidate variables for inclusion in the stepwise regression models.
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