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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

Tue 1/23/2018 3:00PM - 4:00PM
Zhixiang Lin, Stanford University
Public Health A522

Biostatistics guest speaker, Zhixiang Lin, Stanford University, will present, "Statistical methods for high-throughput genomic data."
Thu 1/25/2018 3:30PM - 4:30PM
David Gerard, University of Chicago
Public Health Auditorium (G23)

Biostatistics guest speaker, David Gerard, University of Chicago, will present, " Better Genotyping for Polyploids."
Thu 4/5/2018 3:30PM - 4:30PM
Seonjoo Lee, Columbia University
Public Health Auditorium (G23)

Biostatistics guest speaker, Seonjoo Lee, Columbia University, will present.
Thu 4/12/2018 3:30PM - 4:30PM
Ciprian Crainiceanu, Johns Hopkins University
Public Health Auditorium (G23)

Biostatistics guest speaker, Ciprian Crainiceanu, Johns Hopkins University, will present.
Thu 4/19/2018 3:30PM - 4:30PM
Wensheng Guo, University of Pennsylvania
Public Health Auditorium (G23)

Biostatistics guest speaker, Wensheng Guo, University of Pennsylvania, will present.

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

Contact

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

Bernadette Kapko
bkapko@pitt.edu
412-624-3022

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