3 NEW RESIDUAL ERROR MODELS FOR POPULATION PK PD ANALYSES/

Residual error models, traditionally used in population pharmacokineti c analyses, have been developed as if all sources of error have proper ties similar to those of assay error. Since assay error often is only a minor part of the difference between predicted and observed concentr ations, other sources, with potentially other properties, should be co nsidered. We have simulated three complex error structures. The first model acknowledges two separate sources of residual error, replication error plus pure residual (assay) error. Simulation results for this c ase suggest that ignoring these separate sources of error does not adv ersely affect parameter estimates. The second model allows serially co rrelated errors, as may occur with structural model misspecification. Ignoring this error where the correlation between two errors is assume d to decrease exponentially with the time between them, provides more accurate estimates of the variability parameters in this case. The thi rd model allows time-dependent error magnitude. This may be caused, fo r example, by inaccurate sample timing. A time-constant error model fi t to time-dependent error model is sufficient to improve parameter est imates, even when the true time dependence is more complex. Using a re al data set, we also illustrate the use of the different error models to facilitate the model building process, to provide information about error sources, and to provide more accurate parameters estimates.