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Forward selection logistic regression verified the final model.
The tuberculosis testing and positivity models were implemented using stepwise forward selection logistic regression.
Forward selection logistic regression models were built to identify predictors of clinical improvement of HRQOL after RTX compared to dialysis.
A forward selection logistic regression analysis identified SAPS II, site of sepsis, causative organism and age to be associated with mortality, while fluid treatment had no significant effect (P = 0.3754).
A forward selection logistic regression was conducted to identify independent variables with an effect on mortality and test for a treatment effect (χ-test) after adjusting for these independent variables.
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Forward stepwise selection logistic regression with a p value of 0.15 to enter and remain, built multivariate predictive models of shingles in the past 6 months.
We performed forward selection of logistic regression models, selecting the best model that was subsequently assessed for discrimination and calibration, validated, and converted into a clinical diagnostic chart to benchmark the boundaries of detection from a cohort of 30 patients considered to have LVNC.
Significant risk factors were entered into a forward selection multivariate logistic regression analysis.
A stepwise forward selection (Wald) logistic regression procedure was used to derive the model.
Those risk factors that were significantly associated with breast cancer were entered into a forward selection multivariate logistic regression analysis.
a HDRS, Hamilton depression rating scale for depression; b HAMA, Hamilton anxiety scale for anxiety Significant risk factors were entered into a forward selection multivariate logistic regression analysis.
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