title: Glucose Minimal Model population analysis: likelihood function profiling via Monte Carlo sampling.
abstract: Population kinetic modeling approaches, implemented as nonlinear mixed effects models, are attracting growing interest in many fields of biomedicine thanks to their value in estimating population features from sparsely sampled data. However, their application often entails approximations of the original model function, whose effect is difficult to gauge in general. We apply negative log-likelihood profiling to assess the effect of model approximation on the glucose-insulin Minimal Model, and compare nonlinear mixed-effects approximate methods to two-stage methods. Our preliminary findings suggest that nonlinear mixed effects models provide accurate parameter estimates, but also point out that the reliability of such estimates may be affected by large population variability and small sample size.
authors.affiliation: Department of Information Engineering, the University of Padova, Italy. paolo.denti@dei.unipd.it
authors.forename: Paolo
authors.initials: P
authors.lastname: Denti
authors.forename: Paolo
authors.initials: P
authors.lastname: Vicini
authors.forename: Alessandra
authors.initials: A
authors.lastname: Bertoldo
authors.forename: Claudio
authors.initials: C
authors.lastname: Cobelli
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001786:Blood Glucose / Q000378:metabolism*; D003198:Computer Simulation; D005260:Female; D006801:Humans; D007328:Insulin / Q000097:blood*; D016013:Likelihood Functions; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D009010:Monte Carlo Method; D011157:Population Dynamics; D055815:Young Adult
chemical_list: D001786:Blood Glucose; D007328:Insulin