Visual Predictive Check in Models with Time-Varying Input Function.
The nonlinear mixed effects models are commonly used modeling techniques in the pharmaceutical research as they enable the characterization of the individual profiles together with the population to which the individuals belong. To ensure a correct use of them is fundamental to provide powerful diagnostic tools that are able to evaluate the predictive performance of the models. The visual predictive check (VPC) is a commonly used tool that helps the user to check by visual inspection if the model is able to reproduce the variability and the main trend of the observed data. However, the simulation from the model is not always trivial, for example, when using models with time-varying input function (IF). In this class of models, there is a potential mismatch between each set of simulated parameters and the associated individual IF which can cause an incorrect profile simulation. We introduce a refinement of the VPC by taking in consideration a correlation term (the Mahalanobis or normalized Euclidean distance) that helps the association of the correct IF with the individual set of simulated parameters. We investigate and compare its performance with the standard VPC in models of the glucose and insulin system applied on real and simulated data and in a simulated pharmacokinetic/pharmacodynamic (PK/PD) example. The newly proposed VPC performance appears to be better with respect to the standard VPC especially for the models with big variability in the IF where the probability of simulating incorrect profiles is higher.
Department of Information Engineering, University of Padova, Via G. Gradenigo 6/B, 35131, Padova, Italy.
Anna
A
Largajolli
Department of Information Engineering, University of Padova, Via G. Gradenigo 6/B, 35131, Padova, Italy. bertoldo@dei.unipd.it.
Alessandra
A
Bertoldo
Exploratory Medicine, Merck KGaA, Darmstadt, Germany.
Marco
M
Campioni
Department of Information Engineering, University of Padova, Via G. Gradenigo 6/B, 35131, Padova, Italy.
Claudio
C
Cobelli
D003198:Computer Simulation; D005544:Forecasting; D005947:Glucose / Q000493:pharmacokinetics*; D006801:Humans; D007328:Insulin / Q000493:pharmacokinetics*; D008954:Models, Biological; D017711:Nonlinear Dynamics; D013997:Time Factors
D007328:Insulin; D005947:Glucose
mixed-effect modeling; model diagnostic; models using input function; visual predictive check