Joint modeling of the clinical progression and of the biomarkers' dynamics using a mechanistic model.
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.
Guedj
Jeremie
J
Laboratory of Viral Dynamics, Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan 52900, Israel.
Thiébaut
Rodolphe
R
Commenges
Daniel
D
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
D015415:Biomarkers; D015704:CD4 Antigens; D012367:RNA, Viral