The linear regression mapping algorithms tested generally performed poorly, over-estimating the predicted compared to observed EQ-5D values, especially when observed EQ-5D was below 0.5.
All the simulation results from the different levels of complexity were compared to observed data for the years 1995 and 2003.
The performance of all methods was assessed in several ways: At first, differently corrected RCM data was compared to observed climate data.
This is illustrated in Fig. 6 for a poorly soluble selective glucocorticoid modulator, where total lung dose, predicted using a mouth/throat model, is compared to observed AUC values in healthy volunteers (Olsson & Bäckman 2014).
Finally a description of a numerical simulation model is described with examples of the effects of model variables when both analytical and simulation models are compared to observed data.
Effect sizes (log OR) in ASD analyses for individual measures were 12% higher for data with imputation compared to observed data only although standard errors were 7% higher compared to those expected from the increased sample size.
A number of different options were assessed and compared to observed in vivo data.
Predicted 1 AP time constant = 6.55 ± 0.54 s. (C ) Across 10 cells, the predicted 1 AP time constant based upon linear fit to 25, 50, 100 AP 10 Hz data compared to observed 1 AP time constant.
For the patient view however, lowering of risks is only partly predicted by DHM-2008, since the amount of predicted deaths compared to observed deaths turned out to be relatively low for first-and-only admissions and high for readmissions.
Model output was compared to observed patterns, and the most accurate level of complexity thus determined.
The model results are compared to observed EXAFS, IR, Raman and NMR spectra.
