%0 Journal Article
%T Between-Subject and Within-Subject Model Mixtures for Classifying HIV Treatment Response
%A Cyprien Mbogning
%A Kevin Bleakley
%A Marc Lavielle
%J Progress in Applied Mathematics
%D 2012
%I 
%R 10.3968/j.pam.1925252820120402.s0801
%X We present a method for using longitudinal data  to classify individuals into clinically-relevant population subgroups. This is achieved by treating ``subgroup'' as a categorical covariate whose value is unknown for each individual, and predicting its value using mixtures of models that represent ``typical'' longitudinal data from each subgroup.  Under a nonlinear mixed effects model framework, two types of model mixtures are presented, both of which have their advantages. Following illustrative simulations, longitudinal viral load data for HIV-positive patients is used to predict whether they are responding -- completely, partially or not at all -- to a new drug treatment.
%U http://cscanada.net/index.php/pam/article/view/2922