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Non-significant interactions were dropped from the model using backwards elimination.
We achieved this by conducting a hierarchical linear model, using backwards elimination of effects.
Variables significantly associated with isolation were entered into a binary logistic regression model using backwards elimination.
Variables with p-values less than 0.10 in unadjusted models were entered into the reduced model using backwards elimination.
Variables that, on bivariate analyses, were significantly associated with isolation (p < 0.05) were entered into a binary logistic regression model using backwards elimination [ 23].
We examined a multivariate model using backwards hierarchical elimination [ 19], and one in which we included only the significant (p < 0.05) univariate predictor variables.
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As the primary predictor of interest, HIV serostatus was forced into multivariate models, while other covariates with p-value<0.05 were retained in the final models using backwards selection.
We were not able to model case status with a multivariate model when using backwards hierarchical elimination.
Alternatively, model selection using backwards selection in combination with Akaike Information Criterion was also conducted, and identical results were found.
Multivariate models were derived using backwards selection, starting with a model that included all variables that were significant (p < 0.1) in the univariate analysis, and removing non-significant variables one at a time until only significant variables (p < 0.05) remained.
The model was adjusted using backwards elimination.
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