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Searching for an optimal AUC estimation method: a never-ending task?

Jawień W - J Pharmacokinet Pharmacodyn (2014)

Bottom Line: The models may be given as an explicit C(t) relationship or defined by differential equations.The difference between the optimal estimator and other methods becomes more pronounced with a decrease in sample size or decrease in the variability.It is indicated that many alternative approaches are also possible.

View Article: PubMed Central - PubMed

Affiliation: Faculty of Pharmacy, Jagiellonian University in Kraków, ul. Medyczna 9, 30-688, Kraków, Poland, wojciech.jawien@uj.edu.pl.

ABSTRACT
An effective method of construction of a linear estimator of AUC in the finite interval, optimal in the minimax sense, is developed and demonstrated for five PK models. The models may be given as an explicit C(t) relationship or defined by differential equations. For high variability and rich sampling the optimal method is only moderately advantageous over optimal trapezoid or standard numerical approaches (Gauss-Legendre or Clenshaw-Curtis quadratures). The difference between the optimal estimator and other methods becomes more pronounced with a decrease in sample size or decrease in the variability. The described estimation method may appear useful in development of limited-sampling strategies for AUC determination, as an alternative to the widely used regression-based approach. It is indicated that many alternative approaches are also possible.

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Mean profile for Model 1. Area covered by each symbol is proportional to a contribution of a concentration value at given knot into AUC value estimated by the corresponding method
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Fig5: Mean profile for Model 1. Area covered by each symbol is proportional to a contribution of a concentration value at given knot into AUC value estimated by the corresponding method

Mentions: Figure 4 contains sample spaghetti plots for all models at and . Each 200th PK profile (of 20,000) is shown along with the corresponding concentrations measured at knots of investigated methods. Yet another manner of comparison of methods is displayed in Fig. 5. It contains a “mean” profile for Model 1, i.e. a profile simulated at midpoint values of PK parameters. The symbols are plotted at knots of the corresponding methods. The area covered by each symbol is proportional to its contribution to the total AUC.Fig. 4


Searching for an optimal AUC estimation method: a never-ending task?

Jawień W - J Pharmacokinet Pharmacodyn (2014)

Mean profile for Model 1. Area covered by each symbol is proportional to a contribution of a concentration value at given knot into AUC value estimated by the corresponding method
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4225057&req=5

Fig5: Mean profile for Model 1. Area covered by each symbol is proportional to a contribution of a concentration value at given knot into AUC value estimated by the corresponding method
Mentions: Figure 4 contains sample spaghetti plots for all models at and . Each 200th PK profile (of 20,000) is shown along with the corresponding concentrations measured at knots of investigated methods. Yet another manner of comparison of methods is displayed in Fig. 5. It contains a “mean” profile for Model 1, i.e. a profile simulated at midpoint values of PK parameters. The symbols are plotted at knots of the corresponding methods. The area covered by each symbol is proportional to its contribution to the total AUC.Fig. 4

Bottom Line: The models may be given as an explicit C(t) relationship or defined by differential equations.The difference between the optimal estimator and other methods becomes more pronounced with a decrease in sample size or decrease in the variability.It is indicated that many alternative approaches are also possible.

View Article: PubMed Central - PubMed

Affiliation: Faculty of Pharmacy, Jagiellonian University in Kraków, ul. Medyczna 9, 30-688, Kraków, Poland, wojciech.jawien@uj.edu.pl.

ABSTRACT
An effective method of construction of a linear estimator of AUC in the finite interval, optimal in the minimax sense, is developed and demonstrated for five PK models. The models may be given as an explicit C(t) relationship or defined by differential equations. For high variability and rich sampling the optimal method is only moderately advantageous over optimal trapezoid or standard numerical approaches (Gauss-Legendre or Clenshaw-Curtis quadratures). The difference between the optimal estimator and other methods becomes more pronounced with a decrease in sample size or decrease in the variability. The described estimation method may appear useful in development of limited-sampling strategies for AUC determination, as an alternative to the widely used regression-based approach. It is indicated that many alternative approaches are also possible.

Show MeSH
Related in: MedlinePlus