Bootstrap methods are used in many disciplines to estimate the uncertainty of parameters, in-cluding multi-level or linear mixed-effects models. Residual-based bootstrap methods which resample both random effects and residuals are an alternative approach to case bootstrap, which resamples the individuals. Most PKPD applications use the case bootstrap, for which software is available. In this study, we evaluated the performance of three bootstrap methods (case bootstrap, nonparametric residual bootstrap and para-metric bootstrap) by a simulation study and compared them to that of an asymptotic method in estimating uncertainty of parameters in nonlinear mixed-effects models (NLMEM) with heteroscedastic error. This simulation was conducted using as an example of the PK model for aflibercept, an anti-angiogenic drug. As expected, we found that the bootstrap methods provided better estimates of uncertainty for parameters in NLMEM with high nonlinearity and having balanced designs compared to the asymptotic method, as implemented in MONOLIX. Overall, the parametric bootstrap performed better than the case bootstrap as the true model and variance distribution were used. However, the case bootstrap is faster and simpler as it makes no assumptions on the model and preserves both between subject and residual variability in one resampling step. The performance of the nonparametric residual bootstrap was found to be limited when ap-plying to NLMEM due to its failure to reflate the variance before resampling in unbalanced designs where the asymptotic method and the parametric bootstrap performed well and better than case bootstrap even with stratification.