Estimation and prediction with HIV-treatment interruption data.
We consider longitudinal clinical data for HIV patients undergoing treatment interruptions. We use a nonlinear dynamical mathematical model in attempts to fit individual patient data. A statistically-based censored data method is combined with inverse problem techniques to estimate dynamic parameters. The predictive capabilities of this approach are demonstrated by comparing simulations based on estimation of parameters using only half of the longitudinal observations to the full longitudinal data sets.
Adams
B M
BM
Center for Research in Scientific Computation, North Carolina State University, Box 8205, Raleigh, NC 27695-8205, USA.
Banks
H T
HT
Davidian
M
M
Rosenberg
E S
ES
D023241:Antiretroviral Therapy, Highly Active / Q000379:methods*; D018791:CD4 Lymphocyte Count; D015331:Cohort Studies; D003627:Data Interpretation, Statistical; D004334:Drug Administration Schedule; D015658:HIV Infections / Q000188:drug therapy* / Q000276:immunology; D015497:HIV-1; D006801:Humans; D008137:Longitudinal Studies; D008954:Models, Biological; D011237:Predictive Value of Tests; D019562:Viral Load