The multivariate logistic regression analysis used a backward selection process to eliminate nonsignificant variables from the model (criteria for elimination from the model set at p > 0.05).
The significance levels were set to 0.05 for entry into and 0.10 for elimination from the model.
(1) At the first step, we first included all the aforementioned variables, listed in Table 1, in the multivariable Cox's PH model and then used the stepwise selection procedure, with P<0.05 and P>0.05 for the two-tailed Wald test as the criteria for, respectively, variables inclusion and elimination from the model.
We developed a final model using backward elimination, with variables with p>0.05 eliminated from the model.
The final model was constructed using a manual backward stepwise elimination procedure (p > 0.20): we eliminated from the model the predictors one-by-one on the basis of the highest p-value.
Race, gender, dialysis center, time of entry into the study, serum iron, TIBC, MCV, MCH, serum calcium, and serum phosphorus were eliminated from the model in backward elimination as they failed to reach the level of significance (P < 0.05).
A base model was selected separately for each biomarker using backward elimination starting from the model that included all exposure terms and covariates.
These were inversely associated with faster sterilization phase bacillary elimination from the SSCC model (odds ratio [OR], 0.39; 95% confidence interval [CI],.22 .70) and a faster BER from the TTP model (OR, 0.71; 95% CI,.55 .94).
Subsequently, covariates were excluded by backwards elimination from the full model, if the associated increase in objective function value was not significant at a P value of 0.001.
Using backward elimination from the PRI model for patients 50 years of age or older with an index visit that was not due to a CCI disease, the change in the estimated probability (Δ p ^ ) was 2.8%, 4.3%, and 0.7% when the PRI, age, and sex were removed, respectively.
Again, the model selected by backward elimination (FE9) differed from the model selected by AIC and AICc (FE10).
