Sensitivity analysis of pharmacodynamic parameters in pharmacodynamic models

An extremely strange observation on the equations for calculation of correlation coefficient

Various equations are used to calculate the correlation coefficient, these equations are presumed equally. However we find the extraordinary results when using r = square root of ((sigma(ŷi - y)2) / (sigma(yi - y)2)) and r2 = (sigma(yi - y)2 - sigma(yi - ŷi)2) / (sigma(yi - y)2) to calculate the correlation coefficient, for example, a line within 95% confidence band of a regressed line. The results are so extraordinary that we do not know whether or not we can still call the results as correlation coefficient, however we are sure that these results need to be presented.

Calculation of steady-state distribution delay between central and peripheral compartments in two-compartment models with infusion regimen

A lag time may exist between blood drug concentration and drug effect. Various factors can contribute to the lag time, among which the drug distribution delay is a significant one. The drug distribution delay can also exist between different compartments. An equation was derived to calculate the steady-state drug concentration delay between central and peripheral compartments in a two-compartment model with infusion regimen.

Squared correlation coefficient of measured values versus predicted values in linear and monoexponential regressions

The correlation coefficient of measured values vs predicted values is widely used in pharmacokinetic and biopharmaceutical settings. When using linear and monoexponential regressions, we notice an interesting characteristic of the squared correlation coefficient of measured values vs predicted values, i.e. the squared correlation coefficient of measured y1 vs predicted (lambda)y2 is a constant regardless of different values of regression coefficients and is equal to the squared correlation coefficient of measured x1 vs measured y1.