The Biostatistics seminar series includes research-focused talks by division faculty and other guests. All seminars are free and open to faculty, students and staff.
- See an archive of seminars from 2005 to present.
- See an archive of student presentations from 2005 to present.
- See Biostatistics presentations on Prezi
- March 26, 2014
From 3:30 pm until 5:00 pmDescription: Michael Rosenblum, of the Department of Biostatistics at Johns Hopkins More details...
April 2, 2014
From 3:30 pm until 5:00 pmDescription: Chris Paciorek, Associate Research Statistician in the Department of Statistics More details...
April 16, 2014
From 3:30 pm until 5:00 pmDescription: Jianwen Cai, of the Department of Biostatistics at the University More details...
April 23, 2014
From 3:30 pm until 5:00 pmDescription: Debajyoti Sinha, of the Department of Statistics at Florida State More details...
April 30, 2014
From 3:30 pm until 5:00 pmDescription: Jing Ning, of the Department of Biostatistics at the University More details...
May 7, 2014
From 3:30 pm until 5:00 pmDescription: Abhyuday Mandal, of the Department of Statistics at the University More details...
« March 2014 » M T W T F S S 1 2 3 4 5Events on March 5, 2014
- Biostatistics SeminarStarts: 3:30 pmEnds: March 5, 2014 - 5:00 pmLocation: Location to be determined.Description: Rebecca Andridge, of the Division of Biostatistics at The Ohio State University, will present.
6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26Events on March 26, 2014
- Biostatistics SeminarStarts: 3:30 pmEnds: March 26, 2014 - 5:00 pmLocation: Moos Tower, Room 2-620Description: Michael Rosenblum, of the Department of Biostatistics at Johns Hopkins Bloomberg School of Public Health, will present: "Optimal Tests of Treatment Effects for the Overall Population and Two Subpopulations in Randomized Trials, using Sparse Linear Programming".
We propose new, optimal methods for analyzing randomized trials, when it is suspected that treatment effects may differ in two predefined subpopulations. Such subpopulations could be defined by a biomarker or risk factor measured at baseline. The goal is to simultaneously learn which subpopulations benefit from an experimental treatment, while providing strong control of the familywise Type I error rate. We formalize this as a multiple testing problem and show it is computationally infeasible to solve using existing techniques. Our solution involves first transforming the original multiple testing problem into a large, sparse linear program. We then solve this problem using advanced optimization techniques. This general method can solve a variety of multiple testing problems and decision theory problems related to optimal trial design, for which no solution was previously available. Specifically, we construct new multiple testing procedures that satisfy minimax and Bayes optimality criteria. For a given optimality criterion, our approach yields the optimal tradeoff between power to detect an effect in the overall population versus power to detect effects in subpopulations. We give examples where this tradeoff is a favorable one, in that improvements in power to detect subpopulation treatment effects are possible at relatively little cost in additional sample size. We demonstrate our approach in examples motivated by two randomized trials of new treatments for HIV.
This is joint work with Han Liu (Princeton) and En-Hsu Yen (Intel-NTU), and has been accepted for publication in the Journal of the American Statistical Association (Theory and Methods), and is available online here: http://www.tandfonline.com/doi/full/10.1080/01621459.2013.879063#.UvAwn0JdXpA
A social tea will be held at 3:00 P.M. in A434 Mayo. All are Welcome.
For more details contact 612-624-4655 or see http://www.sph.umn.edu/biostatistics/seminars/
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Division of Biostatistics
A460 Mayo Bldg., MMC 303
420 Delaware St., S.E.
Minneapolis, MN 55455
Director of Graduate Studies
School of Public Health