Estimation and prediction with HIV-treatment interruption data. (English) Zbl 1139.92304
Summary: 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.
MSC:
| 92C50 | Medical applications (general) |
| 62N01 | Censored data models |
| 62P10 | Applications of statistics to biology and medical sciences; meta analysis |
| 62N02 | Estimation in survival analysis and censored data |
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