RE: Visual predictive check!

Andreas, Your simulations highlight a limitation with the combined (additive + proportional or slope-intercept) residual error model. The combined residual error model cannot be the correct model at very low concentrations since the normal distribution will put non-zero probability mass at concentrations less than zero if the mean is low relative to its SD. The purist in me says don't truncate as that will lead to bias in your simulations although it may be minimal if few observations are simulated with negative concentrations. A better approach would be to consider an alternative residual error model that bounds the concentrations to be positive such as the log-normal residual error model (log-transform both sides approach) or fit a model that takes into account the censored BQL data ( see Beal, Ways to Fit a PK Model with Some Data Below the Quantification Limit. JPP 2001;28:481-504). Ken Kenneth G. Kowalski President & CEO A2PG - Ann Arbor Pharmacometrics Group 110 E. Miller Ave., Garden Suite Ann Arbor, MI 48104 Work: 734-274-8255 Cell: 248-207-5082 [EMAIL PROTECTED]