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We took two approaches for each side effect model: a multivariate logistic regression model that included all variables, and a machine learning logistic regression model with feature selection.
Multivariate model 2 included all variables in multivariate model 1 plus smoking.
Multivariate model building using the remaining 16 variables resulted in a final model that included eight variables (Table 3).
Multivariate model including the same variables as the main model for RA.
Multivariate models including patient covariates only had greater explanatory power (C = 0.613) than models with physician variables only (C = 0.566) or copayments only (C = 0.543).
Multivariate models over the entire population were fitted including a missing indicator variable for patients without BMI value.
The initial multivariate models included those variables that had P < 0.4 in univariate regression.
Final multivariate models included country and variables where p≤0.1 for the LRT comparing the multivariate model containing the variable with the model that did not.
c Multivariate models include all the variables in the column.
The reduced multivariate models included all significant variables from the multivariate models.
Finally, a multivariate model including all the variables from the three models together was tested.
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