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Biostatistics Seminars

Department of Biostatistics Seminar Series

The Department of Biostatistics presents a regular speaker series each semester, generally on Thursday afternoon each week. Diverse experts lecture on their work in biostatistics.

Upcoming Biostats Seminars

Thu 10/25/2018 3:30PM - 4:30PM
Dana L. Tudorascu, University of Pittsburgh
Public Health Auditorium (G23)

Dana Tudorascu, Division of General Internal Medicine, Department of Medicine, University of Pittsburgh, will present, “Technical Challenges in the Statistical Analysis of Neuroimaging Studies of Alzheimer's Disease”.
Thu 11/1/2018 3:30PM - 4:30PM
Jingyi (Jessica) Li, University of California, Los Angeles
Public Health Auditorium (G23)

Jingyi (Jessica) Li, Department of Statistics and Department of Human Genetics, University of California, Los Angeles, will present, “Neyman-Pearson Classification Algorithms and NP Receiver Operating Characteristics”.
Thu 11/8/2018 3:30PM - 4:30PM
Hongzhe Lee, University of Pennsylvania
Public Health Auditorium (G23)

Hongzhe Lee, Department of Biostatistics and Epidemiology, University of Pennsylvania, will present, “Optimal Permutation Recovery and Estimation of Bacterial Growth Dynamics”.
Thu 11/29/2018 3:30PM - 4:30PM
Dulal K. Bhaumik, University of Illinois at Chicago
Public Health Auditorium (G23)

Thu 12/6/2018 3:30PM - 4:30PM
Student/Faculty Presentations
Public Health Auditorium (G23)

Previous Biostats Seminars

Biostatistics Seminar Series

Biostatistics Seminar: Michael Wallace, University of Waterloo

Thursday 11/16 3:30PM - 5:00PM
Public Health Auditorium (G23)

Biostatistics Seminar guest speaker, Michael Wallace, University of Waterloo, will present, "Dynamic treatment regimes and reward ignorant modelling".

Personalized medicine optimizes patient outcome by tailoring treatments to patient-level characteristics. This approach is formalized by dynamic treatment regimes (DTRs): decision rules that take patient information as input and output recommended treatment decisions. The DTR literature has seen the development of increasingly sophisticated causal inference techniques, which attempt to address the limitations of our typically observational datsaets. We note, however, that in practice most patients should often receive optimal or near-optimal treatment, and so the outcome used as part of a typical DTR analysis does not provide much additional information. In light of this, we propose reward ignorant modelling: ignoring the outcome and eliciting an optimal DTR by regressing the observed treatment on relevant covariates, as in a more standard analysis. We present some results from investigating this concept, and analysis of data from the International Warfarin Pharmacogenetics Consortium.

Last Updated On Friday, October 27, 2017 by Haydo, Amber LC
Created On Wednesday, August 09, 2017


For information on seminars and events in the department, contact:

Bernadette Kapko

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