Daniel Gillen

“My methodologic research is motivated by applications stemming from a multitude of clinical disciplines including Alzheimer’s Disease, nephrology, and cancer.”

Designing Efficient and Ethical Clinical Trials

A large portion of Professor Dan Gillen’s research focuses on the development of statistical methods for efficiently conducting early- and late-phase clinical trials through the use of interim testing and adaptive procedures. Specific areas of his clinical trials research include recruitment and retention strategies in Alzheimer’s disease prevention and treatment studies, estimation of maximally tolerable doses for new experimental interventions, and sequential testing of weighted survival and longitudinal data. For further information and for software for designing, analyzing, monitoring and reporting sequential clinical trials, visit www.RCTdesign.org. 

Robust Inference for Censored Survival Data

Many biomedical and epidemiologic studies are focused on modeling the time to some event, such time to death or to dementia. These data are often censored so that the true time to event is missing for some individuals. “My research in survival analysis considers the development and understanding of statistical methods for modeling censoring event times when commonly made assumptions are violated,” says Professor Gillen. “This work also includes censoring robust efficient designs for survival studies including the nested case-control design and the case-cohort design.” This research is motivated by multiple collaborations in the areas of Alzheimer’s disease, end-stage-renal disease, and cancer.

Exploring Spatial Health Disparities and Risk Factors

A broad theme of Professor Gillen’s research is the development of statistical methods that yield valid estimation and inference over a wide range of statistical assumptions. “This theme persists in my focus on the development of statistical methods to flexibly estimate the association between spatially and/or temporally correlated environmental exposures and the risk of clinical outcomes in humans,” he says. “Specific research projects include estimating acute air pollutant effects using case-crossover designs with heavily imbalanced data, conducting non-parametric tests of hormetic environmental toxin dose-response curves, and incorporating spatially smoothed environmental exposure into models for censored survival data.”

Education

Ph.D., Biostatistics, University of Washington, 2003

M.S., Biostatistics, University of Washington, 2001

B.S., Mathematics, Cal Poly State University, San Luis Obispo, 1997