Journal of Pharmacokinetics and Pharmacodynamics

, Volume 30, Issue 6, pp 387–404

Simultaneous vs. Sequential Analysis for Population PK/PD Data I: Best-Case Performance

Article

Abstract

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.

pharmacokinetics pharmacodynamics data analysis modeling forcing function 

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Copyright information

© Plenum Publishing Corporation 2003

Authors and Affiliations

  • Liping Zhang
    • 1
  • Stuart L. Beal
    • 2
  • Lewis B. Sheiner
    • 2
  1. 1.Program in Biological and Medical InformaticsUCSFUSA
  2. 2.Department of Laboratory Medicine, School of MedicineUCSFUSA
  3. 3.Department of Biopharmaceutical Sciences, School of PharmacyUCSFUSA

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