Bayesian methods for voriconazole therapeutic drug monitoring (TDM) have been reported previously but there are only sparse reports comparing the accuracy and precision of predictions of published models. Furthermore, the comparative accuracy of linear, mixed linear and non-linear, or entirely nonlinear models may be of high clinical relevance. In this study, models were coded into Individually Designed Optimum Dosing Strategies (ID-ODS™) with voriconazole concentration data analyzed using inverse Bayesian modeling. The data used was from two independent datasets, patients with proven or suspected invasive fungal infections (n=57) and hematopoietic stem cell transplant recipients (n=10). Observed voriconazole concentrations were predicted, where for each concentration value, the data available to that point were used to predict that value. The mean prediction error (ME) and mean squared prediction error (MSE) and their 95% confidence intervals (95%CI) were calculated to measure absolute bias and precision, while delta ME (Δ ME) and delta MSE (Δ MSE) and their 95% CI to measure relative bias and precision, respectively. 519 voriconazole concentrations were analyzed using three models. MEs (95%CI) ranged from 0.09 (-0.02, 0.22), 0.23 (0.04, 0.42) to 0.35 (0.16 to 0.54) while the MSEs (95%CI) from 2.1 (1.03, 3.17), 4.98 (0.90, 9.06), to 4.97 (-0.54 to 10.48) for the linear, mixed, and non-linear models, respectively. In conclusion, while simulations with the linear model were found to be slightly more accurate and similarly precise, the small difference in accuracy is likely negligible from the clinical point of view making all three approaches appropriate for use in a voriconazole TDM program.