Dr. Wei Liu

Department of Mathematics and Statistics

York University

Title: "Semiparametric Nonlinear Mixed-effects Models with Covariate Measurement Errors and Missing Responses"

Abstract:
HIV viral dynamic models have received great attention in recent years. These models approximate
the viral load trajectories after the start of an anti-HIV treatment. The viral dynamic parameters
can be used to evaluate the efficacy of the anti-HIV treatment. Parametric nonlinear mixed-effects
(NLME) models have been widely used to model HIV viral dynamics in the initial period. After the
initial period, however, parametric models may not fit the data well and a semiparametric or
nonparametric model may be more flexible. Therefore, a semiparametric NLME model may be useful for
modeling long-term HIV viral dynamics. In practice, statistical analyses of HIV viral dynamics are
complicated by the following problems: (i) important covariates such as CD4 cell count are often
measured with substantial errors; (ii) missing data often occur in the response (viral load)
measurements due to patients' dropouts or other problems, and the missing data are likely to be
non-ignorable in the sense that the missingness may be related to the missing values; (iii) missing
data may also occur in time-varying covariates due to different measurement schedules or other
problems. We consider likelihood methods which simultaneously address measurement error and missing
data problems in semiparametric NLME models. A real HIV dataset is analyzed in detail, and simulation
studies are conducted to evaluate the methods.