In OD a design criterion is used to link the experimental design to the measure of interest, commonly the joint precision of the parameter estimates. The information richness of an individual may be improved by increasing the quantity of samples or by increasing the quality per sample, e.g. Conversely, if the individual information is rich the prior population information will have smaller influence and the predicted EBEs will be closer to the “true” individual values. If little information is provided about the individual parameters the patient will be regarded as a typical representative of the population and the predicted EBEs will be close to the typical population predictions, an effect known as η-shrinkage. Characterization of individual parameters can also be of importance for establishing concentration-effect relationships. Good precision of the EBEs are therefore of importance for effective model evaluation and for understanding and determination of individual differences in PK and PD. model diagnostics, covariate analysis and feedback dose individualization. Individual parameter estimates, referred to as Empirical Bayes Estimates (EBEs), can be derived by Maximum a Posteriori (MAP) estimation and are of interest in e.g. With a Bayesian approach individual and occasion deviations from the typical population parameters can be estimated given a population model, its population parameter estimates, and individual observations. between dosing occasions or observation periods) is apparent IOV could be introduced as a third level of random effects. Typically in pharmacokinetic (PK) and pharmacodynamic (PD) analyses inter individual variability (IIV) and residual error (RE) are estimated, but if variability between occasions (e.g. The NLME approach splits the model in fixed effects describing the typical population value parameters and different levels of random effects. Inter occasion variability (IOV) is increasingly quantified in nonlinear mixed effect (NLME) models, but the impact of this type of variability on the optimal experimental design (OD) for the estimation of individual parameters is not clear. For the investigated designs, the MAP occ method was on average slightly superior to POP occ and was less computationally intensive. In addition MAP occ and POP occ accurately predicted precision and shrinkage. Accounting for IOV in the FIM MAP markedly affected the designs compared to ignoring IOV and, as evaluated by stochastic simulation and estimation, resulted in superior precision in the individual parameters. Sparse sampling schedules were designed for two test models and compared to a scenario where IOV is ignored, either by omitting known IOV (Omit) or by mimicking a situation where unknown IOV has inflated the IIV (Inflate). In this work two methods of including IOV in the maximum a posteriori Fisher information matrix (FIM MAP) are evaluated: (i) MAP occ-the IOV is included as a fixed effect deviation per occasion and individual, and (ii) POP occ-the IOV is included as an occasion random effect. IOV may adversely affect the precision of maximum a posteriori (MAP) estimated individual parameters, yet the influence of inclusion of IOV in optimal design for estimation of individual parameters has not been investigated.
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Inter occasion variability (IOV) is of importance to consider in the development of a design where individual pharmacokinetic or pharmacodynamic parameters are of interest.