Jun Zhang of the Department of Biostatistics defends her dissertation on "Interpretable Analysis of Multivariate Functional Data".
The Joint Statistical Meetings, known simply as "JSM", is the largest gathering of statisticians held annually in North American. Faculty and student presenters from the Department of Biostatistics regularly participate giving invited talks, contributed talks, and poster presentations. Our students often receive top awards and participate in the affiliated career marketplace at the event.
Meetings of the Eastern North American Region of the International Biometric Society (a.k.a. "ENAR meetings") are held in late March or early April each year and reflect the broad interests of the Society, including both quantitative techniques and application areas. Faculty and student presenters from the Department of Biostatistics regularly participate giving invited talks, contributed talks, and poster presentations.
Constructing Dynamic Treatment Regimes for Binary Outcome with Partially SMART data
A dynamic treatment regime is a sequence of decision rules that specify how the dosage and/or type of treatment should be adjusted through time in response to an individual's changing needs. Q-learning, which involves an iterative two-step procedure that first uses regression to model the conditional mean outcome at the each stage, and second, derives the estimated regime by maximizing the estimated conditional mean functions, is often used on data from SMART studies to develop the optimal treatment regime. We propose to generalize Q-learning to the case of binary outcome with data from a partial SMART study, where only a proportion of patients went through the full course of the trial. The method will be illustrated using data from a web-based smoking cessation study.
Last Updated On Thursday, May 9, 2019 by Borkowski, Matthew Gerard
Created On Thursday, April 21, 2016
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