Held on Wednesday, October 24th, 2012 at the Crowne Plaza Hotel in Northbrook, IL.
The Program consisted of three presentations:
- A nonparametric empirical Bayes framework for large-scale multiple testing Ryan Martin, Ph.D., Assistant Professor, University of Illinois at Chicago
- Statistics in Drug Safety: The curious case of Antidepressants, Anticonvulsants, and Suicide Robert Gibbons, Ph.D., Professor, University of Chicago
- Pseudo-value regression models in survival analysis Brent Logan, Ph.D., Professor, Medical College of Wisconsin
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A nonparametric empirical Bayes framework for large-scale multiple testing Given byRyan Martin, Ph.D., Assistant Professor, University of Illinois at Chicago
Abstract This talk will present a flexible and identifiable version of the two-groups model, motivated by hierarchical Bayes considerations, that features an empirical null and a semiparametric mixture model for the non-null cases. A computationally efficient predictive recursion marginal likelihood procedure is used to estimate the model parameters, even the nonparametric mixing distribution. This leads to a nonparametric empirical Bayes testing procedure, called PRtest, based on thresholding the estimated local false discovery rates.
Simulations and real-data examples demonstrate that, compared to existing approaches, PRtest's careful handling of the non-null density can give a much better fit in the tails of the mixture distribution which, in turn, may lead to more realistic conclusions. (This is joint work with Professor Surya T. Tokdar at Duke University.)
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Statistics in Drug Safety: The curious case of Antidepressants, Anticonvulsants, and Suicide Given by Robert Gibbons, Ph.D., Professor, University of Chicago
Abstract In 2003, the U.S. FDA, MHRA in the U.K., and European Union released public health advisories for a possible causal link between antidepressant treatment and suicide in children and adolescents ages 18 and under. This led the U.S. FDA to issue a black box warning for antidepressant treatment of childhood depression in 2004, which was later extended to include young adults (18-24) in 2006. Following these warnings, rather than observing the anticipated decrease in youth suicide rates, record increases in youth suicide rates were observed in both the U.S. and Europe. In this presentation, we review the data and statistical methodology that led to the public health advisories and black box warning, and the data that led to the record increases in youth suicide rates and discuss their possible relationship. New statistical and experimental design approaches to post-marketing drug safety surveillance are developed, discussed and illustrated.
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Pseudo-value regression models in survival analysis Given by Brent Logan, Ph.D., Professor, Medical College of Wisconsin