Harrell's c statistic was calculated for both the model using stage alone and the comprehensive model using additional covariates.
Two additional patient cohorts (n = 1,991) were used as an external validation, with the comprehensive model again outperforming the model using stage alone with regards to prognostic stratification and the three evaluated metrics.
The comprehensive model consistently outperformed the model using stage alone in prognostic stratification and on Harrell's C, Nagelkerke's R2, and Brier Scores in the whole patient population as well as in specific treatment modalities.
Two models, one using stage alone and another comprehensive model using additional covariates, were constructed.
To analyze the over/underrepresentation of taxonomies between different Tanner stage groups, we used a linear model using Tanner staging as a two-level predictor variable and the subsampled proportions as the dependent variable.
We analyse our model using two-stage Stackelberg game-theoretic approach where the supplier acts as a Stackelberg leader.
In the study of Adeney, Ennett, Frize and Korenberg [ 9], this methodology is used and a model using two-stage ANN is presented.
The nonequilibrium-stage model using the Maxwell Stefan equations, the nonequilibrium-stage model using effective diffusion coefficients, the equilibrium-stage model including reaction kinetics, and the equilibrium-stage model assuming chemical equilibrium were investigated.
A logistic regression model using a one-stage cluster design (birth hospital) was used for this analysis.
In this paper, we propose to use a new approach in learning RFs model using a two-stage quality-based SNP subspace selection method, which is specifically tailored for high dimensional data of GWA studies.
Using staging models in the IMRT cohort, we failed to identify better stage migrations than TNM6th T-classification and TNMc2008 N-classification.
