title: Markov modelling of immunological and virological states in HIV-1 infected patients.
abstract: The purpose of this study was to evaluate the evolution of HIV infected patients and to bring out some significant factors associated with this pathology. The main criteria revealing the State of illness is viral load measurement (VL). However the CD4 lymphocytes also represent an important marker as these reflect the State of the immune reservoir. Many studies have been carried out in this field and different models have been proposed with a view to a better understanding of this disease. Multi State Markov models defined in terms of CD4 counts, or in terms of viral load, have proved to be very useful tools for modelling HIV disease progression. The model we have developed in this study is based on both the CD4 lymphocytes counts and VL. Markov models are characterized by transition intensities. In this paper we explored several structures in succession. First, we used a homogeneous continuous time Markov process with four states defined by crossed values of CD4 and VL in a given patient at a given time. Then, the effect of certain covariates on the infection process was introduced into the model via the transition intensity functions, as with a Cox regression model. Since the hypothesis of homogeneity may be unrealistic in certain cases, we also considered piecewise homogeneous Markov models. Finally, the effects of covariates and time were combined in a piecewise homogeneous model with a covariate. We applied these methods to data from 1313 HIV-infected patients included in the NADIS cohort.
authors.affiliation: Clinical Research University Institute, Biostatistics Laboratory, 641 avenue D.G. Giraud, 34093 Montpellier, France. emathieu@iurc.montp.inserm.fr
authors.forename: E
authors.initials: E
authors.lastname: Mathieu
authors.forename: P
authors.initials: P
authors.lastname: Loup
authors.forename: P
authors.initials: P
authors.lastname: Dellamonica
authors.forename: J P
authors.initials: JP
authors.lastname: Daures
mesh_terms: D000367:Age Factors; D000465:Algorithms; D018791:CD4 Lymphocyte Count; D018450:Disease Progression; D005260:Female; D015658:HIV Infections / Q000276:immunology / Q000821:virology*; D015497:HIV-1; D006801:Humans; D016013:Likelihood Functions; D008297:Male; D008390:Markov Chains; D015233:Models, Statistical; D015203:Reproducibility of Results; D012737:Sex Factors; D013997:Time Factors; D019562:Viral Load