Additional multivariable models considered adjustment for clinical characteristics: stage (I, II, III, IV), grade (I, II, III), radiation treatment (yes/no), and chemotherapy/hormonal therapy (no chemotherapy or hormonal therapy, hormonal therapy and no chemotherapy, chemotherapy and no hormonal therapy, hormonal therapy and chemotherapy).
Multivariable models, considering all possible combinations of predictors and covariates, with the smallest Akaike information criterion were selected as the final model for each distress outcome.
The relationships between baseline GFR, or GFR changes from baseline to month 6, and subsequent GFR decline were evaluated by two multivariable models considering as outcomes the GFR slopes calculated throughout the whole follow-up period (model 1) or from month 6 to study end (model 2), respectively.
Findings from the multivariable logistic models considering the four low health literacy-LEP combination groups (adjusted for control variables) for each screening type are provided in table 3.
Possible presence of multicollinearity in multivariable models was considered if the variance inflation factor exceeded 4. Best-fitting GEE models were judged using the Quasilikelihood Information Criterion, the measure developed for GEE models analogous to the Akaike Information Criterion.
When all significant variables from our multivariable models were considered, year, log10 total number of hogs processed per year, and an interaction between year and census agricultural region were significantly associated with partial condemnation rates for lung condemnations in our multivariable model (Table 3).
Confounding and effect modification were then considered for the variable of community type, and other socio-demographic factors originally omitted from the multivariable model but considered to be potential mechanisms necessary to understanding the factors associated with household participation in the protective practices.
Based on the use of subspace system identification algorithms a dynamic multivariable model, which considered the permeate flow rate and the permeate conductivity as the controlled variables, is derived and its parameters are computed from experimental data.
In the multivariable model we considered as significant those variables with p < 0.05.
In the multivariable model we considered significant variables with biological importance.
Variables significantly associated (p≤0.05) with cardiac risk score use in the multivariable model were considered important in predicting risk score adherence.
