Three models, linked in series, can be used to analyze combined pharmacokinetic (PK) and pharmacodynamic (PD) data arising from non--steady-state experiments. A PK model relates dose to plasma drug concentration (Cp); a link model relates Cp to drug concentration at the effect site (Ce); and a PD model relates Ce to drug effect (E). All three submodels can be stated parametrically. Recently the use of a nonparametric PD submodel has been proposed (CLIN PHARMACOL THER 1984;35:733-41). In this article we use an extended nonparametric approach that represents both the PK and PD models nonparametrically, but retains a parametric link model. Cp data from several PK models and E data from several PD models were simulated. After the addition of noise to both the Cp and E data, they were analyzed by both the parametric and extended nonparametric methods. The methods were compared by how well they estimated the PD model. To assess robustness, the effect of misspecification of the PK submodel on the goodness of estimation of both methods was also compared. In the absence of model misspecification, the parametric method usually estimates the PD model better than the nonparametric method. However, this difference in the performances diminishes and even reverses when the PK model is misspecified. Because one can rarely be certain that model misspecification is absent, the nonparametric approach may offer a distinct advantage for routine analysis of PK/PD data.