Contacts

About:

Dr. Joel A. Dubin is co-founder and former co-lead (and now lead) of the Health Data Science Lab and an Associate Professor in both the Department of Statistics & Actuarial Science, and the School of Public Health and Health Systems, at the University of Waterloo. He earned his B.A. in Psychology & Statistics from Rutgers University, an M.S. in Applied Statistics from Villanova University, and M.S. and Ph.D. in Statistics (in 2000) from the University of California, Davis, under the supervision of Han-Georg Müller. Joel was an Assistant Professor in the Division of Biostatistics at Yale University, from 2000 until 2005, when he arrived at U. of Waterloo.

His primary research interest is in the area of methodological development in longitudinal data analysis, including for multivariate longitudinal data, where more than one outcome, (e.g., systolic and diastolic blood pressure, or two distinct measures of smoking behavior) are each followed for individuals over time. Methods pursued for this type of data include the correlation of different longitudinal outcomes over time using curve-based methods, and incorporating lags and derivatives of the curves. He is also interested in change point, latent response models, and prediction models for (sometimes multivariate) longitudinal data.

An additional area of interest is in data visualization, including developing graphical methods for censored survival data, which simultaneously display individual-level as well as cohort-level survival information. This research has resulted in two separate graphical methods, the event chart and the event history graph; the latter displays time-dependent covariate information embedded within the Kaplan-Meier survival curve. These methods can be found in the R functions event.chart and event.history, respectively, which are in Frank Harrell’s Hmisc package.

He works in a variety of application areas, including nephrology, cancer, smoking cessation, aging, environmental issues, nutrition, physical activity, and the analysis of adverse events from clinical trials.