Effect Of Nonmem Minimization Status And Number Of Replicates On Bootstrap Parameter Distributions For Population Pharmacokinetic Models: A Case Study

Aims Bootstrap (BS) parameter distributions are often used to characterize estimation uncertainty and determine confidence intervals (CI) for population pharmacokinetic (PPK) model parameters. These results are used to guide inferences about clinical relevance of covariate effects and other model components. The goal of this work was to compare BS parameter distributions using a published PPK model for oxaprozin (OX) under different minimization and re-sampling conditions. Methods Nonparametric BS analyses with NONMEM were conducted on a PPK model for OX and resulting parameter distributions were summarized by: 1) number of BS replicates (REPS), and 2) minimization (MIN) and $COVARIANCE (COV) status. Results For those runs reporting parameter estimates, BS CI for all parameters 1) did not change by more than 9% after 1000 BS REPS; 2) were unaffected by MIN status (<5% change), and most CI were unaffected by COV status (<5% change in all but 1 parameter). Conclusions The number BS REPS should be investigated for each problem, but a general estimate of 1000 REPS may be a useful starting point. MIN status did not affect BS CI for this case. Clinical Pharmacology & Therapeutics (2005) 77, P2–P2; doi: 10.1016/j.clpt.2004.11.010