title: Joint modeling of the clinical progression and of the biomarkers' dynamics using a mechanistic model.
abstract: Joint models are used to rigorously explore the relationship between the dynamics of biomarkers and clinical events. In the context of HIV infection, where the multivariate dynamics of HIV-RNA and CD4 are complex, a mechanistic approach based on a system of nonlinear differential equations naturally takes into account the correlation between the biomarkers. Using data from a randomized clinical trial comparing dual antiretroviral therapy to a single drug regimen, a full maximum likelihood approach is proposed to explore the relationship between the evolution of the biomarkers and the time to a clinical event. The role of each marker as an independent predictor of disease progression is assessed. We show that the joint dynamics of HIV-RNA and CD4 captures the effect of antiretroviral treatment; the CD4 dynamics alone is found to capture most but not all of the treatment effect.
authors.affiliation: Laboratory of Viral Dynamics, Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan 52900, Israel.
authors.forename: Jeremie
authors.initials: J
authors.lastname: Guedj
authors.forename: Rodolphe
authors.initials: R
authors.lastname: Thiébaut
authors.forename: Daniel
authors.initials: D
authors.lastname: Commenges
mesh_terms: D015415:Biomarkers / Q000097:blood; D001699:Biometry / Q000379:methods*; D015704:CD4 Antigens / Q000097:blood*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D018450:Disease Progression; D015658:HIV Infections / Q000097:blood* / Q000175:diagnosis / Q000453:epidemiology*; D006801:Humans; D015233:Models, Statistical; D012367:RNA, Viral / Q000097:blood*; D018570:Risk Assessment / Q000379:methods; D012307:Risk Factors; D014481:United States / Q000453:epidemiology
chemical_list: D015415:Biomarkers; D015704:CD4 Antigens; D012367:RNA, Viral