Simultaneous vs. Sequential Analysis for Population PK/PD Data I: Best-Case Performance
Rent the article at a discountRent now
* Final gross prices may vary according to local VAT.Get Access
Dose [-concentration]-effect relationships can be obtained by fitting a predictive pharmacokinetic (PK)-pharmacodynamic (PD) model to both concentration and effect observations. Either a model can be fit simultaneously to all the data (“simultaneous” method), or first a model can be fit to the PK data and then a model can be fit to the PD data, conditioning in some way on the PK data or on the estimates of the PK parameters (“sequential” method). Using simulated data, we compare the performance of the simultaneous method with that of three sequential method variants with respect to computation time, estimation precision, and inference. Using NONMEM, under various study designs, observations of one type of PK and one type of PD response from different numbers of individuals were simulated according to a one-compartment PK model and direct Emax PD model, with parameters drawn from an appropriate population distribution. The same PK and PD models were fit to these observations using simultaneous and sequential methods. Performance measures include computation time, fraction of cases for which estimates are successfully obtained, precision of PD parameter estimates, precision of PD parameter standard error estimates, and type-I error rates of a likelihood ratio test. With the sequential method, computation time is less, and estimates are more likely to be obtained. Using the First Order Conditional Estimation (FOCE) method, a sequential approach that conditions on both population PK parameter estimates and PK data, estimates PD parameters and their standard errors about as well as the “gold standard” simultaneous method, and saves about 40% computation time. Type-I error rates of likelihood ratio test for both simultaneous and sequential approaches are close to the nominal rates.
- D. M. Foster. Developing and testing integrated multicompartment models to describe a single-input multiple-output study using the SAAM II software system. Adv. Exp. Med. Biol. 445:59-78 (1998).
- J. Bennett and J. Wakefield. Errors-in-variables in joint population pharmacokinetic/ pharmacodynamic modeling. Biometrics 57:803-812 (2001).
- Y. Hashimoto and L. B. Sheiner. Designs for population pharmacodynamics: value of pharmacokinetic data and population analysis. J. Pharmacokinet. Biopharm. 19:333-353 (1991).
- J. Shi, N. L. Benowitz, C. P. Denaro, and L. B. Sheiner. Pharmacokinetic-pharmacodynamic modeling of caffeine: tolerance to pressor effects. Clin. Pharmacol. Ther. 53:6-14 (1993).
- K. E. Fattinger, D. Verotta, H. C. Porchet, A. Munafo, J. Y. Le Cotonnec, and L. B. Sheiner. Modeling a bivariate control system: LH and testosterone response to the GnRH antagonist antide. Am. J. Physiol. 271:E775-787. (1996).
- J. R. Wade and M. O. Karlsson. In Population Approach Group Europe, Saintes, France, (1999).
- L. Sheiner and J. Wakefield. Population modelling in drug development. Stat. Methods Med. Res. 8:183-193 (1999).
- M. Davidian and D. Giltinan. Nonlinear Models for Repeated Measurement Data. Chapman & Hall, London (1995).
- H. C. Kimko, S. S. Reele, N. H. Holford, and C. C. Peck. Prediction of the outcome of a phase 3 clinical trial of an antischizophrenic agent (quetiapine fumarate) by simulation with a population pharmacokinetic and pharmacodynamic model. Clin. Pharmacol. Ther. 68:568-577 (2000).
- J. C. Wakefield, L. Aarons, and A. Racine-Poon. The Bayesian approach of population pharmacokinetic/pharmacodynamic modeling Springer-Verlag, (1998).
- L. Zhang, R. Price, F. Aweeka, S. E. Bellibas, and L. B. Sheiner. Making the most of sparse clinical data by using a predictive-model-based analysis, illustrated with a stavudine pharmacokinetic study. Eur. J. Pharm. Sci. 12:377-385. (2001).
- M. Gibaldi and D. Perrier. Pharmacokinetics. Marcel Dekker, New York (1982).
- M. L. Stein. Large sample properties of simulations using Latin hypercube sampling. Technometrics 29:143-151 (1987).
- A. B. Owen. Controlling correlations in Latin hypercube samples. J. Am. Stat. Assoc. 89:1517-1522 (1994).
- S. L. Beal and L. B. Sheiner. NONMEM Users Guides. Globomax, Inc., Maryland. (1989-1998).
- A. R. Gallant. Nonlinear Statistical Models. John Wiley & Sons, Inc., New York, p. 217(1986).
- Simultaneous vs. Sequential Analysis for Population PK/PD Data I: Best-Case Performance
Journal of Pharmacokinetics and Pharmacodynamics
Volume 30, Issue 6 , pp 387-404
- Cover Date
- Print ISSN
- Online ISSN
- Kluwer Academic Publishers-Plenum Publishers
- Additional Links
- data analysis
- forcing function
- Industry Sectors