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.
Andrew Topp of the Department of Biostatistics defends his dissertation on "Doubly Robust Estimation in Two-stage Dynamic Treatment Regimes in the Presence of Drop-out"
Graduate faculty of the University and all other interested parties are invited to attend.
Various methods exist for causal inference about the effects of different treatments from observational studies or randomized trials. A straightforward approach is to fit a regression model of the outcome as a function of the treatment they received along with observed patient characteristics. Other methods, such as inverse probability weighting, work by instead estimating a patient's probability of receiving treatment and weighting the outcomes by the inverse of this probability. Doubly Robust estimators use both models and provide unbiased estimates as long as either the probability of treatment or outcome is correctly modeled. These techniques can be extended to analyze and compare dynamic treatment regimes, that is, multiple stages of treatment punctuated by decision points concerning what the next treatment should be. This dissertation is concerned with developing more efficient doubly robust estimators for two-stage dynamic treatment regimes, first without and later, with data missing at random. First, we develop a new inverse probability of treatment weighted and doubly robust estimators for analyzing dynamic treatment regimes. Then we compare these methods to the corresponding existing methods for estimating the mean outcome of dynamic treatment regimes in a simulation. We utilize the new doubly robust estimator in the analysis of the STAR*D trial to estimate the mean outcome of patients on different regimes for the treatment of non-psychotic major depressive disorder. Finally, we propose a modification of the new inverse probability of treatment weighted and doubly robust estimators in order to account for missing data.
Last Updated On Friday, March 17, 2017 by Valenti, Renee Nerozzi
Created On Monday, August 22, 2016