Speaker: Xinmiao Wang
Title: Nonlinear Mixed Effects Models with Missing Time-dependent Covariates
Abstract: HIV viral dynamic models have been used to describe the virus elimination and production process during antiviral treatments. These models have received great attention in the literature and have been shown to perform well in many AIDS studies. Nonlinear mixed effects (NLME) models have been proposed for modelling HIV viral dynamic. However, missing data in time-dependent covariates often lead to challenges in statistical analysis of data for HIV viral dynamics. We propose a new multiple imputation method to deal with the missing time-dependent covariates problems in NLME models. The proposed method is used to analyze a real HIV dataset and compared to the naïve complete case method, and somewhat different conclusions are obtained based on the proposed multiple imputation method.
Speaker: Xinzhe Dong
Title: A Multiple Imputation Method for Missing Responses in NLME Models
Abstract: Missing data frequently arise in longitudinal studies. There has been extensive research in this area. However, further research is still needed for certain models for longitudinal data with missing values. Understanding the missing data and handling them properly is essential for the validity of statistical inference. This project focuses on one of the missing data methods, the multiple imputation method, and extends this method to impute the missing responses in nonlinear mixed effects models. Simulations are conducted to evaluate the performance of the proposed method. An AIDS study is presented as an example, in which the viral loads are modeled by an HIV viral dynamic model.