Jon Wakefield
Imperial College, London, UK
Population models contain large numbers of parameters and, for each of the individuals, a nonlinear relationship between observed concentrations and unknown parameters. Consequently inference for population data is not straightforward. In this talk the main population approaches will be described and compared. On the modelling front the distinction between parametric and nonparametric models will be made. On the inference front likelihood-based and Bayesian approaches will be described. The approaches I will describe in detail will be linearization [1,3], nonparametric maximum likelihood [4], smooth nonparametric maximum likelihood [2] and a Bayesian parametric approach [5].
References:
[1] Beal, S.L. and Sheiner, L.B. (1982). Estimating population kinetics. CRC Critical Reviews in Biomedical Engineering, 8, 195-222.
[2] Davidian, M. and Gallant, A.R. (1992). Smooth nonparametric maximum likelihood estimation for population pharmacokinetics, with application to quinidine. Journal of Pharmacokinetics and Biopharmaceutics, 20, 529-556.
[3] Lindstrom, M.J. and Bates, D.M. (1990). Nonlinear mixed effects models for repeated measures data. Biometrics, 46, 673-687.
[4] Mallet, A. (1986). A maximum likelihood estimation method for random coefficient regression models. Biometrika, 73, 645-656.
[5] Wakefield, J.C., Smith, A.P.M., Racine-Poon, A. and Gelfand, A.E. (1994). Bayesian analysis of linear and nonlinear population models using the Gibbs sampler. Applied statistics, 43, 201-221.
Reference: PAGE 3 (1994) Abstr 880 [www.page-meeting.org/?abstract=880]
Poster: oral presentation