The aim of this study was to compare 2 stepwise covariate model-building strategies, frequently used in the analysis of pharmacokinetic-pharmacodynamic (PK-PD) data using nonlinear mixed-effects models, with respect to included covariates and predictive performance. In addition, the effects of stepwise regression on the estimated covariate coefficients were assessed. Using simulated and real PK data, covariate models were built applying (1) stepwise generalized additive models (GAM) for identifying potential covariates, followed by backward elimination in the computer program NONMEM, and (2) stepwise forward inclusion and backward elimination in NONMEM. Different versions of these procedures were tried (eg, treating different study occasions as separate individuals in the GAM, or fixing a part of the parameters when the NONMEM procedure was used). The final covariate models were compared, including their ability to predict a separate data set or their performance in cross-validation. The bias in the estimated coefficients (selection bias) was assessed. The model-building procedures performed similarly in the data sets explored. No major differences in the resulting covariate models were seen, and the predictive performances overlapped. Therefore, the choice of model-building procedure in these examples could be based on other aspects such as analyst- and computer-time efficiency. There was a tendency to selection bias in the estimates, although this was small relative to the overall variability in the estimates. The predictive performances of the stepwise models were also reasonably good. Thus, selection bias seems to be a minor problem in this typical PK covariate analysis.