Mingyao Li, PhD, Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania.
Peter Mueller, PhD, Department of Mathematics, Department of Statistics and Data Sciences, University of Texas at Austin
Lu Mao, PhD, Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison
Snehalata Huzurbazar, PhD, Department of Biostatistics, West Virginia University
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.
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.
Geoffrey Johnson of the Department of Biostatistics defends his dissertation on "Quality Adjusted Q-learning and Conditional Structural Mean Models for Optimizing Dynamic Treatment Regimes"
Graduate faculty of the University of Pittsburgh and all other interested parties are invited to attend.
The focus of this work is to investigate a form of Q-learning using estimating equations for the quality adjusted survival outcome, and generalize these methods to quality adjust other outcomes. We use the m-out-of-n bootstrap and threshold utility analysis to show how the patient-specific optimal regime varies according to the treatment characteristics (e.g. cost, side effects). Methodologies investigated are demonstrated to construct optimal treatment regimes for the treatment of children's neuroblastoma. We also propose a new method for optimizing dynamic treatment regimes using conditional structural mean models. The inverse-probability of-treatment weighted (IPTW) or g-computation estimator is used at each stage to estimate what we call the 'preliminary' optimal treatment regime, given patient information up to the current stage and prior treatment assignment. Essentially this tailors the optimal treatment assignment at the current stage, and provides an optimal strategy for the remaining stages given the information currently available. We compare this method for optimizing a dynamic treatment regime to Q-learning. Additionally, we proposed a two step prescriptive variable selection procedure that supports the tailored optimization of dynamic treatment regimes using conditional structural mean models by eliminating from consideration any suboptimal treatment regimes and sifting out the covariates that prescribe the optimal treatment regimes.
The methods described herein are meant to advance the field of dynamic treatment regimes, a field that has a substantial impact on public health. The treatment policies that come from DTRs, whether determined for the population as a whole or tailored for specific subgroups, can be used to guide and shape health policies that will ultimately lead to greater public health and safety.
Last Updated On Monday, September 12, 2016 by Valenti, Renee Nerozzi
Created On Monday, April 11, 2016