Department of Biostatistics
Colloquium Series

2014-2015


Organizer: Sebastien Haneuse
Coordinator: Meagan Plante

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September 18 - (Kresge G2, 4:00 - 5:30 pm)

Myrto Lefkopoulou Distinguished Lecture

Tianxi Cai, Sc.D.
Professor of Biostatistics, Department of Biostatistics, Harvard School of Public Health


"Discovery Research with Electronic Medical Records"

September 25 (BIO Conference Room, 12:30 - 2:00 pm)

Michael Rosenblum, Ph.D.
Assistant Professor, Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health

"Optimal Tests of Treatment Effects for the Overall Population and Two Subpopulations in Randomized Trials, using Sparse Linear Programming"
ABSTRACT: 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. Below we give an image representing the rejection regions for an optimal procedure that will be discussed in the talk. This is joint work with Han Liu (Princeton) and En-Hsu Yen (University of Texas at Austin), which has been accepted for publication in the Journal of the American Statistical Association (Theory and Methods).
October 31 (FXB G13, 1:30 pm)

Lagakos Distinguished Alumni Award

Jesse Berlin, SD
Vice President of Epidemiology at Janssen Research & Development, LLC

"Perspectives of a Recovering Academic Biostatistician: Transitions and Lessons Learned about Statistics and Beyond"

November 6 (FXB G12, 12:30 - 2:00 pm)

Karl Broman, Ph.D.
Professor, Department of Biostatistics and Medical informatics, University of Wisconsin

"Interactive Graphics for High-dimensional Genetic Data"
ABSTRACT: The value of interactive graphics for making sense of high-dimensional data has long been appreciated but is still not in routine use. I will describe my efforts to develop interactive graphical tools for genetic data, using JavaScript and D3. (The tools are available as an R package: R/qtlcharts, http://kbroman.org/qtlcharts)

I will focus on an expression genetics experiment in the mouse, with gene expression microarray data on each of six tissues, plus high-density genotype data, in each of 500 mice. I argue that in research with such data, precise statistical inference is not so important as data visualization.
February 19 (BIO Conference Room, 12:30 - 2:00 pm)

Susan Paddock, Ph.D.
Senior Statistician, RAND Corporation

"A Hierarchical Bayesian Approach for Estimating Statistical Benchmarks of Health Care Provider Performance"
ABSTRACT: The public reporting of performance standards is central to efforts to monitor and improve health care provider quality. One approach is to set performance targets, or statistical benchmarks, that define a high level (e.g., top 10%) of observed provider performance. Widely-used approaches to setting benchmarks often summarize direct estimates of hospital performance, such as the observed proportion of instances when a provider delivers a particular type of care. Benchmarks might be unduly affected by high-variance direct estimates of provider performance. While provider-specific posterior means offer more stable estimates, concerns have been raised about basing benchmarks on such estimates. In this talk, I will discuss how the identification of providers that exceed a performance benchmark is a question that requires fully considering the distribution of performance across all providers, and note that the two aforementioned approaches instead focus on the distribution of summaries of hospital-specific performance. Using publicly available data from the Medicare Hospital Compare website, I will illustrate how widely-used statistical benchmarks of provider performance compare to those obtained by estimating the empirical distribution function of provider performance using hierarchical Bayesian modeling. In this analysis, there was variation across statistical benchmarking approaches with respect to which providers exceeded the top 10% benchmark, but not for the 50% threshold. The results illustrate that benchmarks derived from the histogram of provider performance under hierarchical Bayesian modeling provide a compromise between benchmarks based on direct estimates, which are over-dispersed relative to the true distribution of provider performance and prone to high variance for small providers, and posterior mean provider performance, for which under-dispersion relative to the true provider performance distribution is a concern. Given the rewards and penalties associated with characterizing top performance, the ability of statistical benchmarks to summarize key features of the provider performance distribution should be further examined.
March 26 (BIO Conference Room, 12:30 - 2:00 pm)

Ellen Wijsman, Ph.D.
Professor, Division of Medical Genetics and Department of Biostatistics, University of Washington

"Talk Title TBD"
ABSTRACT: None Given
April 16 (BIO Conference Room, 12:30 - 2:00 pm)

Marc Suchard, M.D., Ph.D.
Professor in the Departments of Biomathematics and of Human Genetics in the David Geffen School of Medicine, UCLA

"Talk Title TBD"
ABSTRACT: None Given
May 7 (BIO Conference Room, 12:30 - 2:00 pm)

Yingye Zheng, Ph.D.
Member, Fred Hutchinson Cancer Research Center, University of Washington

"Talk Title TBD"
ABSTRACT: None Given


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