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These factors were then included in a multivariate model (binary logistic regression, Table 5).
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In general, conditional on M z, all models are reduced to standard multivariate models for binary and normally distributed traits.
The independence of the predictors of the MetS was tested in the multivariate models using binary logistic regression.
For all multivariate regression models (binary logistic, Cox) the models were selected with the backward elimination with sls (significance level to stay) = 0.2 starting with all potential predictors listed for the model.
To allow inclusion in our multivariate model, we created a binary variable for participation in clinical trials (low versus high participation), by using a cutoff at 5% of expected patients being entered into trials.
Due to sample size limitations, we used the continuous ADSI score as the dependent variable for the multivariate model rather than the binary indicator (meets/does not meet CDC criteria).
A multivariate model was structured using binary logistic regression (expressed as odds ratios [OR] and 95% confidence intervals [95%CI]), inserting potential confounding variables significant in univariate chi-square (all categorized) to analyze the association between physical activity and healthcare expenditures.
Based on the multivariate linear regression (MLR) a multivariate model was developed using robust binary particle swarm optimization (PSO) for the feature selection step.
Missing values were imputed in the multivariate model using Stata's mi impute command with the logistic regression method for binary variables.
It is a multivariate model.
Multivariate model.
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