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Using method SD, the values of the parameters describing residual error are lower than for method VAR, but the values of the structural parameters and their inter-individual variability are hardly affected by the choice of the method.īoth methods are valid approaches in combined proportional and additive residual error modelling, and selection may be based on OFV. ETAs in the original model are replaced with new added THETAs to obtain the standard errors of these added THETA estimates. Parameters, and are fixed at the values obtained from the estimation step for all the subjects. The different coding of methods VAR yield identical results. To do this, PsN Internally generates modified NONMEM control stream and dataset for each subject. Using datasets from literature and simulations based on these datasets, the methods are compared using NONMEM. Look at it this way - the usual mixed proportional and constant error model. Method SD assumes that the standard deviation of the residual error is the sum of the proportional and additive components. You are using a trick to make NONMEM do something it wasnt designed to do. Race represented by values 0, 1, 2, 3, 4 and we use above type proportional covariate model.
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Method VAR assumes that the variance of the residual error is the sum of the statistically independent proportional and additive components this method can be coded in three ways. Lets say if we have a covariate with more categories, i.e.
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The theoretical background of the methods is described. Different approaches have been proposed, but a comparison between approaches is still lacking. In pharmacokinetic modelling, a combined proportional and additive residual error model is often preferred over a proportional or additive residual error model.