While we agree that it is important to validate uptake, we argue that validation of uptake through a particular pathway using a positive control is much more critical in cases where no differences between the treatment and control can be observed, as for example in the direct feeding studies by Alvarez-Alfageme et al. [8].
For each new HPV DNA test, it is important to validate its clinical performance using established tests as benchmarks.
When this kind of retrofitting of a model to the data is done, it is important to validate the modified model in a separate (cross-validation) sample; this was done in CFA-4 using an independent sample of upper-level nursing students from one Ontario university (n=132) as well as with data from our main sample regarding their classroom experiences.
First, it is important to validate a patient's depression and anxiety and assess whether they are caused by the substances.
It is important to validate this prior to CFD simulations.
Therefore, it is important to validate the current protocols [5].
It is important to validate the estimated petrophysical parameters using the core data.
However, for this approach to be successful it is important to validate the models used in the simulations to obtain the necessary degree of confidence.
Before embarking on a meaningful real-field problem, it is important to validate the GA code, the problem formulation for history matching and the proposed scheme, and methodology of history matching.
Thus, it is important to validate this assumption by simulating the full physics of the rays for typical optical systems to see how the full solution differs from the approximation.
The selection of optimal training and test set sizes has been discussed in several papers [25, 26] leading to the conclusion that while it is important to validate a QSAR model with a good external set, if the splitting leads to a training set smaller than 75-85% of the original data set, the modeling quality may be affected.
