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This will be followed by fitting univariable models.
To decide which variables should be included in this multivariable model, an exploratory analysis was performed by fitting univariable models and considering as candidates for the multivariable model, all variables significant at the 0.15 significance level.
To decide which variables should be included in the multivariable models, an exploratory analysis was performed by fitting univariable models and considering as candidates for the multivariable model all variables significant at the 0.10 significance level in these univariable models.
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Variables were screened for inclusion into the multivariable logistic regression model by fitting univariable logistic regression models, without random intercepts, and those predictor variables with a liberal significance level (α = 0.2) were selected.
Like Toyoda et al (2006), we fitted univariable Cox regression models to verify the set of prognostic parameters and confirmed that statistical significance was maintained when entered into a multivariable model.
To account for the matched design, we fitted univariable conditional logistic regression models to assess associations between each variable of interest and ALL.
We fitted univariable and multivariable logistic regression models to assess for relationships between gender and our outcome of interest, before and after adjustment for confounding variables.
We fit univariable and multivariable logistic regression models to investigate associations between TB infection and gender, before and after adjusting or possible confounding factors, including ability to produce sputum, age and residence.
§Calculated by fitting the model to data.
This analysis is done by fitting a logistic regression model.
We tested the continuous variables maternal age and BMI for deviations from linearity by fitting functional polynomials in the univariable logistic regression models with multiple transformations of the continuous variables.
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