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Better Living Through Statistics

April 20-21, 2018 at the University of Rochester, New York


Keynote Speaker

Dr. Bhramar Mukherjee, University of Michigan

Dr. Bhramar Mukherjee photo

Dr. Mukherjee is a professor at the University of Michigan  

  • Friday, April 20, 4:00 to 5:30 p.m.—Dr. Mukherjee will give a seminar sponsored by the Department of Biostatistics

Revisiting the Genomewide Threshold of  in 2018                                                 

During the past two years, there has been much discussion and debate around the perverse use of the P-value threshold of 0.05 to declare statistical significance for single null hypothesis testing. A recent recommendation by many eminent statisticians is to redefine statistical significance at P<0.005 [Benjamin et al, Nature Human Behaviour, 2017]. This new threshold is motivated by the use of Bayes Factors and true control of false positive report probability. In genomewide association studies, a much smaller threshold of  has been used with notable success in yielding reproducible results while testing millions of genetic variants. I will first discuss the historic rationale for using this threshold. We will then investigate whether this threshold that was proposed about a decade ago needs to be revisited with the current genomewide data we have in terms of the newer sequencing platforms, imputation strategies, testing rare versus common variants, the existing knowledge we have gathered regarding true association signals, or for controlling other metrics associated with multiple hypotheses testing beyond the family wise error rate. I will discuss notions of Bayesian error rates for multiple testing and use connections between the Bayes Factor and the Frequentist Factor (the ratio of power and Type 1 error) for declaring new discoveries. Empirical studies using data from the Global Lipids Consortium will be used to evaluate if we applied various thresholds/decision rules in 2008 or 2009, how many of the most recent GWAS results (in 2013) would we detect and what would be our “true” false discovery rate. This is joint work with Zhongsheng Chen and Michael Boehnke at the University of Michigan.

  • Saturday, April 21, 4:45 to 5:55 p.m.—Plenary Lecture 

How Big Data can Leverage Small Data and Conversely   

While reviewing a recent article by a famous statistician from Harvard University, I was struck by the following sentence: “Seeing scientific applications turn into methodological advances is always a joy, at least for those of us who care about advancing the science of data, in addition to advancing science with data.” In this talk, I will try to share this “joy” (and associated anxiety) of being a quantitative scientist at a time when our science and society are undergoing unprecedented information/data revolution. I will present three ideas/examples: (1) Shrinkage estimation to combine heterogeneous data sources; (2) Expanding an existing risk prediction model with auxiliary summary information that might be publicly available; (3) A phenomewide association study with polygenic risk scores and electronic health records using data from the Michigan Genomics Initiative, a longitudinal biorepository at Michigan Medicine. The examples are designed to illustrate that principled study design and data science methodology are at the heart of doing good science with data. This is joint work with many students and colleagues at University of Michigan.