Yangxin Huang, Ph.D.
2129, MDC 56
Education and History
Came to USF
B.S. Wuhan University of Technology, 1982
M.S. Huazhong University of Science and Technology, 1987
Ph.D. Liverpool John Moores University, 2000
Bayesian Modeling and MCMC
Mixed Effects Models for Repeated Measurements
Joint Modeling for Longitudinal and Survival Data
Longitudinal Data Analysis
HIV/AIDS Clincial Research
Health Data Analysis
Biostatistics and its applications to public health
Bayesian methodology and Markov Chain Monte Carlo
Joint models for longitudinal and survival data
Mixed effects models for repeated measurement data
Missing and measurement error data modeling and analysis
Modeling ODE dynamic system for health research
Modeling biological systems via PBPK/PD ODE models
Clinical research of infectious diseases
Clinical evaluation of HIV/AIDS treatments
Dr. Huang's current research interests are (1) Development of various statistical models and associated statistical methods to analyze longitudinal, repeated measurements, missing, censoring and survival data from epidemiological, medical and health fields. (2) Joint models with skew distributions for longitudinal and survival data: Normality (symmetric) of the model random errors is a routine assumption for the mixed-effects models in many longitudinal studies, but it may be unrealistic obscuring important features of subject variations. Propose a class of models with considering model errors to be a skew distribution for joint behavior of longitudinal dynamic response process, an associated covariate process with measurement errors in conjunction with survival process. Bayesian parametric and nonparametric NLME modeling approaches are proposed to simultaneously estimate model parameters for statistical inference. (3) HIV dynamic modeling: Propose mathematical/statistical models-based a system of ordinary differential equations (ODE) for drug exposure (pharmacokinetics and adherence), drug susceptibility (resistance), drug efficacy and responses of antiretroviral therapies in clinical trials and health data; develop statistical inference methods including Bayesian sampling techniques (MCMC) to estimate parameters in ODE dynamic models. Dr. Huang has also conducted on (1) various biostatistics methods and applications for various medical and health data; (2) interval estimation methods of median effective dose (ED50) for binary response data and their robustness in the presence of model misspecification; (3) optimal designs for the choice of numbers of doses in bioassay and their robustness under model misspecification.
Dr. Huang is a member of the American Statistical Association, International Chinese Statistical Association and the Royal Statistical Society. Dr. Huang serves as the Associate Editor of Journal “Computational Statistics and Data Analysis” and the Guest Editor of “Journal of Probability and Statistics”.