Symmetry and coverage of confidence intervals for a population PK model.
Lars Lindbom, Mats O. Karlsson, E. Niclas Jonsson
Uppsala University, Sweden
Standard error estimates give an indication of the quality of model parameter estimates and are useful tools in hypothesis testing. It is therefore important to evaluate the coverage and symmetry properties of confidence intervals derived from standard error estimates given by standard population modeling regression tools. The aim of this study was to assess these properties of some standard errors given by NONMEM.
The FO method of NONMEM was used for simulation and estimation of a one-compartment pharmacokinetic model with proportional and additive residual error model and IV-bolus administration. Data set size varies in number of subjects (25, 50, 100, 250, 600 and 1000) and number of samples per subject (2, 3 and 4). 2000 point and interval estimates of structural and variance parameters was recorded for all permutations of these data set sizes as well as 20, 30, 40 and 50 % magnitude of proportional error (CV). Bias in point estimates as well as coverage and symmetry in interval estimates at the 80, 90 and 95% confidence level was computed.
Under the assumptions regarding algorithm and model structure, better coverage, symmetry and bias are obtained with increasing number of subjects given the number of samples for each subject. The size of the proportional error does not seem to affect the quality of the bias, coverage and symmetry.