- survival analysis
- clinical trials
- missing data methods
- causal inference
- semiparametrics
Most of my research is motivated by problems I have encountered while working as a clinical trials statistician for cooperative
groups in cancer and AIDS and more recently on clinical trials in cardiology. Since the primary endpoint in such chronic disease
clinical trials is time to an event, often right censored, I have studied specialized methods for the design of such studies
and the analysis of the data. Because of ethical as well as practical reasons, data are monitored periodically and, if there
is sufficient evidence of treatment effect (or lack thereof), a clinical trial may be stopped early. Much of my research has
considered the statistical consequences of stopping rules. Recently, I have been working on efficient designs for stopping
studies which will, on average, stop a study as early as possible while still preserving the statistical accuracy desired.
- outcomes research
- cost-effectiveness analyses
- propensity score methods
- quality-of-life analyses
In clinical research, the randomized trial is considered the gold-standard for comparing treatments. Randomization guarantees
that the treatment groups are balanced on average. My interest is in the more common situation where patients are not
randomized to treatments. In these situations, the treatment decisions are determined by the patient's characteristics and
preferences and the groups of patients are not necessarily similar. I'm interested in a variety of statistical techniques that
reduce bias caused by non-random treatment selection. In addition to hard clinical end-points such as mortality and heart
attacks, I'm interested in the analysis of medical costs and quality-of-life.
