Modeling Markers of Disease Progression by a Hidden Markov Process: Application to Characterizing CD4 Cell Decline

• Published in: Biometrics Vol. 56; no. 3; pp. 733 - 741
• Main Authors: Guihenneuc-Jouyaux, Chantal, Richardson, Sylvia, Longini, Ira M. Jr
• Format: Journal Article
• Language: English
• Published: Oxford, UK Oxford, UK : Blackwell Publishing Ltd 01.09.2000; Blackwell Publishing Ltd; International Biometric Society; Wiley
• Subjects: AIDS; AIDS related complex; Algorithms; Bayesian hierarchical model; Biomarkers; biometry; Biometry - methods; CD4 cells; CD4 Lymphocyte Count; clinical trials; disease course; Disease models; Disease Progression; Gibbs sampling; HIV; HIV Infections - drug therapy; HIV Infections - immunology; HIV Infections - physiopathology; Humans; Markov chain; Markov chain Monte Carlo; Markov Chains; Markov models; Markov process; Markov processes; Measurement error; Models, Statistical; Monte Carlo method; Multilevel models; Multistate models; Parametric models; Patient Compliance; patients; Statistical discrepancies; therapeutics
• ISSN: 0006-341X; 1541-0420
• DOI: 10.1111/j.0006-341X.2000.00733.x

Multistate models have been increasingly used to model natural history of many diseases as well as to characterize the follow-up of patients under varied clinical protocols. This modeling allows describing disease evolution, estimating the transition rates, and evaluating the therapy effects on progression. In many cases, the staging is defined on the basis of a discretization of the values of continuous markers (CD4 cell count for HIV application) that are subject to great variability due mainly to short time-scale noise (intraindividual variability) and measurement errors. This led us to formulate a Bayesian hierarchical model where, at a first level, a disease process (Markov model on the true states, which are unobserved) is introduced and, at a second level, the measurement process making the link between the true states and the observed marker values is modeled. This hierarchical formulation allows joint estimation of the parameters of both processes. Estimation of the quantities of interest is performed via stochastic algorithms of the family of Markov chain Monte Carlo methods. The flexibility of this approach is illustrated by analyzing the CD4 data on HIV patients of the Concorde clinical trial.