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The results of the final multivariate model conducted to identify the predictors associated with LTF are shown in Table 2.
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An additional multivariate model was conducted analyzing WBC count as a continuous variable.
The multivariate model was conducted using a forward stepwise (Wald chi-square) method with a p value <0.05 for entry and a p value ≥ 0.10 for removal.
The risk of UCI did not significantly change with enrollment year (Table 2).> -wrap-foot> Antiretroviraliral Therapy, IRR Incidence Rate Ratio aAge is modeled in 10-year increments + n = 2,000 with complete data on all variables in multivariate model We conducted a multivariate analysis including variables significant in the bivariate analyses.
We then identified candidate variables for our multivariate model by conducting bivariate logistic regression analyses.
Multivariate modeling was conducted for the primary analysis.
Multivariate modeling was conducted by stepwise forward logistic regression, using all covariates significantly associated with typhoid fever in univariate analysis.
We also examined interactions among significant markers in multivariate models and conducted a stratified analysis when significant interactions were detected.
These variables were entered into multivariate logistic regression models conducted using the backward stepwise method: first, all the variables have been included, then, using a stepwise procedure, the variables with not statistically significant association have been removed.
Variables with p value less than 0.10 were included in the multivariate model, which was conducted using a stepwise forward technique.
Multivariate modelling will be conducted using biological considerations regarding which variables should be included in the model, as well as statistical methods of variable selection, such as stepwise regression, deviance comparisons of various candidate models, and goodness-of-fit assessments.
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CEO of Professional Science Editing for Scientists @ prosciediting.com