The agreement between the fitting parameters obtained for the PC index and those reported before for solar wind magnetic field fluctuations is within 30%; which is noticeably less than the difference between the same parameters of solar wind and the AE-index fluctuations.
Furthermore, to avoid an overlap between the same parameters in different variables, we decided not to use severity-score variables to match patients; in fact, both APACHE II and SAPS III scores include parameters of severity for respiratory or cardiac failure, and demographics such as age, which were introduced independently in the matching.
Implementation of a fixed relative bioavailability of 100% for the population but allowing for between-subject variability and between-dose occasion variability (i.e., modeled as within-subject variability between doses) in the same parameter was also evaluated.
In order to improve the comparability of graphs, the mapping of values to colours should aim for consistency between graphs displaying the same parameter, as shown in Figure 3 which underlines the recommendation for unified colour scales.
We will evaluate the level of agreement between two of the same parameter using agreement tables, scatter plots and Cohen's κ statistic.
Implementation of a fixed relative bioavailability of 100% for the population but allowing for between-subject variability and between-dose occasion variability in the same parameter improved the model fit significantly (ΔOFVs of −46.1 and −160, respectively, when implemented sequentially).
The main issue a researcher faces in this common situation is the choice between alternative estimators of the same parameter (cut-point).
Computational efficiency in MCMC schemes is related to the degree of mixing or autocorrelation between subsequent samples of the same parameter.
These correlations, occasionally called test-retest correlations [ 60], represent the relation between repeated measures of the same parameter over time (i.e. how much the score for a parameter can be predicted from its value in a previous cycle).
The ESS determines the effective number of independent draws such that a greater degree of autocorrelation between subsequent samples for the same parameter would lead to a smaller ESS and hence poorer computational efficiency.
