These conditions were then modelled using a finite difference mathematical model and the accuracy of the model assessed by comparison with the experimental results.
We investigated whether a risk factor that minimally improves the AUC may nevertheless improve the predictive ability of the model, assessed by integrated discrimination improvement (IDI).
No detrimental effects of high Ca were observed on bone modeling (assessed by histomorphometry and bone markers), at least in this short-term intervention.
Despite high receptor expression and homogenous receptor density in our model, assessed by biodistribution and in vitro autoradiography [21], high-resolution in vivo SPECT imaging revealed non-uniform uptake patterns of radioactivity within tumor tissue [20, 22, 23].
The overall discrimination performance of this model assessed, by a ROC curve, was 0.72 (95% CI (0.69 0.75)).
The overall fit of the model, assessed by the comparative fit index, was good [CFI = 0.97, root mean square error of approximation (RMSEA) = 0.05, χ/d.f. = 1.85].
To address this, the effect of LDL was explored by adjusting the final linear regression model arrived at through backwards elimination for LDL to assess whether this improved the model (assessed by comparison of model R, BIC and AIC).
The strength of evidence for site, branch and branch-site models was assessed by comparing codeml models "M8" with "M7", "Mfree" with "M0", and "M2" with "M1", respectively.
The model fit of all models was assessed by using the likelihood ratio test.
Performances of the developed models were assessed by simulation with the validation data set.
