In this multivariate model, the strongest independent predictors of survival were (in order of statistical significance) overall stage, patient age, vascular invasion, and SI score.
For the multivariate models, the independent variables that were significant on univariate analyses were included.
In the sense of solving the problems attributed to multivariate models, the classical univariate prediction models (UPMs) were developed.
After running this data through a multivariate model, the authors were able to calculate the factors that best predicted whether a marriage would end in divorce.
For these multivariate models, the best fit RMSE is obtained from SARIMAX 0,1,2)(1,0,0) with LST, rainfall and relative humidity as covariates.
However, for the multivariate model, the effect of household density was reversed and definitive conclusions could not easily be made.
Therefore, in the final multivariate model, the multibacillary form was substantially associated with higher detection rates.
However, by including smoking in the multivariate model the explained variance did only increase with 1%%.
When BMI is included in the multivariate model, the association is attenuated (0.49 [0.22–1.10]; P = 0.09).
In a final multivariate model, the most consistent predictor of erythrocyte isotope incorporation was hepcidin.
