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Demographic, anthropometric and family characteristics were added to each model in stepwise fashion, and the fit of the model was assessed after the inclusion of each variable using area under the curve (AUC) estimated from the receiver operating characteristic curve.
Potential confounding variables (including lake, village, age, sex, presence of other infections, anemia, behavior, and previous treatment) were introduced into the logistic regression model in stepwise manner, and models were compared by using likelihood ratio tests.
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Non-significant (p > 0.05) fixed effect terms were dropped from the models in stepwise backward fashion, after having evaluated the results of Type III F-test.
The independent variables were entered into the regression model in a stepwise fashion, and the same results were obtained from stepwise, forward, and backward regression approaches.
In the interest of clarity we construct the model in a stepwise manner.
These predictors were therefore removed from the model in a stepwise manner.
Non-significant terms were dropped from the full model in a stepwise manner to leave a minimal model, given in Table 1.
Variables were entered in the multivariable model in a stepwise forward fashion.
Multivariate analysis was performed using Cox proportional hazard regression model in forward stepwise regression.
Independent prognostic factors were analysed by Cox proportional hazards regression model in a stepwise manner.
For multivariate analysis, variables were incorporated into the model in a stepwise manner.
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