Faming Liang

University of Florida

Bayesian Neural Networks for Personalized Medicine

Complex diseases such as cancer have often heterogeneous responses to treatment, and this has attracted much interest in developing individualized treatment rules to tailor therapies to an individual patient according to the patient-specific  characteristics. In this talk, we discuss how to use Bayesian neural networks to achieve this goal, including how to select disease related features.

Thursday, November 12, 2015 - 3:30pm

Robert Tibshirani

Stanford University

The Lasso: An Application to Cancer Detection and Some New Tools for Selective Inference

First, I will review the lasso method and show an example of its utility in cancer diagnosis via mass spectometry. Then I will consider the testing the significance of the terms in a fitted regression, fit via the lasso or forward stepwise regression. I will present a novel statistical framework for this problem, one that provides p-values and confidence intervals that properly account for the inherent selection in the fitting procedure. I will give other examples of this procedure, including graphical models and PCA, and describe an R language package for its computation.

Friday, October 30, 2015 - 9:30am
The Georgia Center for Continuing Education

Karen Kafadar

University of Virginia

The Critical Role of Statistics in Development and Validation of Forensic Methods

Statistics has played a key role in the development and validation of forensic methods, as well as in the inferences (conclusions) obtained from forensic evidence.  Further, statisticians have been important contributors to many areas of science, such as chemistry (chemometrics), biology (genomics), medicine (clinical trials), and agriculture (crop yield), leading to valuable advances that extend to multiple fields (spectral analysis, penalized regression, sequential analysis, experimental design).  The involvement of statistics specifically in forensic science has demonstrated its value in

Thursday, October 8, 2015 - 3:30pm

William Brenneman

Procter & Gamble Company

Practicing Statistics in Corporate R&D
While many problems faced in industry can be solved with known statistical methods, there are problems encountered that require original research. For a research statistician practicing in industry, these types of problems are a joy to encounter and an opportunity to contribute to the research literature. I will discuss several examples of opportunities identified in my career at Procter & Gamble, some of which led to subsequent statistical research. The problems will be framed in easy to understand language, with just enough technical details to appreciate the work. I will also discuss how both hard and soft skills are needed to be successful in the corporate world.

William Brenneman is a Research Fellow at Procter & Gamble in the Global Statistics and Data Management Department and an Adjunct Professor at Georgia Tech in the Industrial and Systems Engineering Department.  Since joining P&G in 2000, he has worked on a wide range of projects that deal with statistics applications in his areas of expertise: design and analysis of experiments, robust parameter design, reliability engineering, statistical process control, computer experiments, and general statistical thinking.  He was also instrumental in the development of an in-house statistics c

Tuesday, April 14, 2015 - 3:30pm

Zhongxue Chen

Indiana University

Comparison of Multiple Hazard Rate Functions

Many robust tests have been proposed in the literature to compare two hazard rate functions, however, very few of them can be used in cases when there are multiple hazard rate functions to be compared. In this paper, we propose an approach for detecting the difference among multiple hazard rate functions. Through a simulation study and a real-data application, we show that the new method is robust and powerful in many situations, compared with some commonly used tests.

Thursday, November 19, 2015 - 3:30pm

Susan Murphy

University of Michigan

Micro-randomized Trials & mHealth

Micro-randomized trials are trials in which individuals are randomized 100's or 1000's of times over the course of the study.   The goal of these trials is to assess the impact of momentary interventions, e.g. interventions that are intended to impact behavior over small time intervals.  A fast growing area of mHealth concerns the use of mobile devices for both collecting real-time data, for processing this data and for providing momentary interventions.   We discuss the design and analysis of these types of trials.

Friday, April 24, 2015 - 6:00pm
Athens Botanical Gardens

Lisa McShane

National Cancer Institute, National Institutes of Health

Reproducible Research: Many Dimensions and Shared Responsibilities

Irreproducible biomedical research is particularly concerning because flawed findings have the potential to make their way to clinical studies involving human participants.  Many factors have been suggested as contributors to irreproducibility, including poor study design, analytic instability of measurement methods, sloppy data handling, inappropriate and misleading statistical analysis methods, improper reporting or interpretation of results, and on rare occasions, outright scientific misconduct.

Thursday, April 16, 2015 - 3:30pm


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