Inference in HIV dynamics models via hierarchical likelihood

Author(s):
Commenges, D.; Jolly, D.; Drylewicz, J.; Putter, H.; Thiebaut, R.
Published
2011
Publication type
Article / Letter to editor

Abstract

HIV dynamical models are often based on non-linear systems of ordinary differential equations (ODE), which do not have an analytical solution. Introducing random effects in such models leads to very challenging non-linear mixed-effects models. To avoid the numerical computation of multiple integrals involved in the likelihood, a hierarchical likelihood (h-likelihood) approach, treated in the spirit of a penalized likelihood is proposed. The asymptotic distribution of the maximum h-likelihood estimators (MHLE) for fixed effects is given. The MHLE are slightly biased but the bias can be made negligible by using a parametric bootstrap procedure. An efficient algorithm for maximizing the h-likelihood is proposed. A simulation study, based on a classical HIV dynamical model, confirms the good properties of the MHLE. The method is applied to the analysis of a clinical trial. (C) 2010 Elsevier B.V. All rights reserved.

Algorithm Asymptotic Differential equations h-likelihood HIV dynamics models Non-linear mixed effects model Penalized likelihood mixed-effects models generalized linear-models aids clinical-trials parameter-estimation in-vivo infection responses fitness virus
Date issued
2011-01-01
Language
English
Is part of
Computational Statistics and Data Analysis (2011), Vol. 55, no. 1, p. 446-456
hdl
1887/109280
doi
10.1016/j.csda.2010.05.012