By Peter L. Bonate PhD, FCP (auth.)
This publication offers either the paintings and technology at the back of pharmacokinetic-pharmacodynamic modeling. utilizing a building-block procedure, the writer starts off from linear and nonlinear versions on the person point and proceeds to improve extra complicated linear and nonlinear combined results types on the inhabitants point, with specific emphasis on exhibiting the interrelationships among some of the version varieties. the speculation at the back of the equipment are illustrated utilizing actual information drawn from the literature and from the author’s personal reviews in drug improvement. info are analyzed utilizing quite a few software program, together with SAS, WinNonlin, SAAM II, and NONMEM. A key part of the ebook is to teach how types are authorised and rejected, eventually resulting in an invaluable and informative version that may be applied utilizing desktop simulation to reply to "what-if" questions.
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Extra info for Pharmacokinetic-Pharmacodynamic Modeling and Simulation
K10 Eq. can be approximated by C¼ Dk0 t Àk0 t e V (1:75) where k’ is a hybrid estimate of ka and k10 (Zhi, 1990). Numerical identifiability also becomes a problem with a poorly or inadequately designed experiment. For example, a drug may exhibit multi-exponential kinetics but due to analytical assay limitations or a sampling schedule that stops sampling too early, one or more later phases may not be identifiable. Alternatively, if sampling is started too late, a rapid distribution phase may be missed after bolus administration.
Burnham and Anderson (2002) raise a number of issues regarding the AIC that bear repeating. First, the AIC cannot be used to compare different datasets. AIC can only be compared using the same data set, otherwise a modeler is comparing apples to oranges. Second, AIC values cannot be compared on the same dataset if one of the models was based on a transformation of the dependent variable. For example, it is unfair to compare the AIC based on Y to another model based on Ln(Y), even if they are the same dataset.
45) since the models have different log-likelihood functions. Fourth, AIC can be used to compare non-nested models, which is an extreme advantage over the frequentist approach that has no such easily calculated test for comparisons. Fifth, AIC is not a ‘‘test,’’ there are no pvalues or statistical power or a-levels associated with a particular value of AIC. Sixth, AIC is invariant to 1:1 transformations of the dependent variable. Lastly, the value of an AIC tells nothing about how good the model is.