Variables associated with the outcomes at a level of p < .10 in unadjusted analyses were entered into the final multivariable models after assessing for the presence of multicollinearity.
For risk factor analysis, stepwise backward logistic regression was used to fit the best model after assessing and controlling for interaction and confounding.
For multivariate analysis using log binomial regression, variables with a p-value of less than 0.2 after the bivariate analysis, and those of importance from a clinical standpoint, were maintained in the final model, after assessing the effect of confounding and interaction.
The initial model will be estimated using competing risks Cox proportional hazards regression with death from a non-CVD cause considered a competing risk; alternative model specifications may need to be considered after assessing validity of model assumptions.
After assessing the model output, inoculant dose ratios of 1 1, 1 2 and 1 6 TetR/EryR were selected to test experimentally, because the model suggested an identifiable trend with these doses would be observed.
After assessing all model fit statistics, the SF-36 was eventually split into a 10-item Physical Functioning scale (items 3a-3j) and a 5-item Mental Health scale (items 9b, c, d, f, h).
TN model gives stable variable importance rankings after assessing the relative importance of predictors.
After assessing the confounder model, single air pollution or temperature lags were added, and the effects were estimated linearly.
After assessing the FE and RE models, we used the Hausman test to check best-fitted models in this study.
The best suited architecture was selected after assessing the performance of 480 ANN models involving twelve different architectures.
