Using the model, we have quantitatively assessed parameters of biological and clinical significance including antigen concentration, internalization rates, and efflux rates of antibody from tumor.
The performances of different predictive models, including also clinically assessed parameters, have been considered.
We examined the practical identifiability of the model by assessing parameter determinability in the six individuals which the model could explain.
In this study, an inexact-variance hydrological modeling system (IVHMS) is developed for assessing parameter uncertainty on modeling outputs in the Kaidu River Basin, China.
Regardless of the method used to parameterize the model, decision analysts seek to assess parameter uncertainty and to explore the relative influence of uncertain assumptions made.
Equally, it is possible to assess parameter uncertainty for a specific energy model.
The model-assessed parameters for ISR at 9 mmol/l glucose and beta cell glucose sensitivity were significantly increased with both doses of canagliflozin (Table 5).
In addition, the validity of the PLS-DA model was assessed by statistical parameters: the correlation coefficient R and the cross-validation correlation coefficient Q. R represents the goodness of the fit to the model and Q reveals the predictability of the model [ 22].
The strength of the phylogenetic signal in each model was assessed using the parameter λ which measures and controls for the degree of phylogenetic dependence in the model residuals [42], [43], estimated using a maximum-likelihood approach.
The statistical quality of the QSAR models was assessed using statistical parameters r2, r2cv (cross-validated r2), r2pred (predictive r2) and lack of fit measure (LOF).
Group differences in the magnitude of PD were evaluated separately for each mixture PD model according to the respective model parameters assessing the magnitude of PD.
