- model selection
- bootstrap and permutation methods
I work in a variety of areas that intersect with biomedical research. Currently my major focus is on model selection and
model checking. For example, given a study of 100,000 subjects where the response variable Y is presence (=1) or
absence (=0) of a particular disease and 500 independent X variables, what is the best model relating the probability
of disease P(Y=1) to some function of a subset of the 500 X variables? Then using the selected model, estimate the
probability that a particular person (not in the study) will have the disease and give a measure of reliability of the estimate.
- longitudinal data analysispopulation
- pharmacokinetics
- joint modeling of longitudinal and survival data
- missing data
I have worked extensively in the area of pharmacokinetics, the study of "what the body does to the drug." This is facilitated
by fitting a statistical model describing concentrations of drug in the body over time in terms of quantities characterizing how
the body absorbs, metabolizes, and eliminates drug and how these quantities vary across people. I also work on general methods for
analyzing longitudinal data (data collected over time). For example, the effectiveness of two anti-hypertensive medications may
be compared on the basis of how and to what extent they reduce diastolic blood pressure over the period of study. Data collected
over time on human subjects are often missing, because subjects fail to show up for their scheduled study visits, and I have
worked on methods for analysis that take this into account.
- longitudinal data analysis
- clinical trials
- epidemiology
- survival analysis
Dr. Zhang received his PhD in Biostatistics from the University of Michigan in 1998, and he spent two years as a Senior Research
Associate in the Department of Epidemiology at the University of Michigan before joining the NCSU Department of Statistics in
1998. He has broad interests in many areas of biostatistics, and he teaches or has taught the Deaprtment's clincial trials
course, a survival analysis course, the categorical data analysis course, and several PhD special topics courses on new developments
in one of his main research areas, mixed effects models.
