William DuMouchel, PhD
Chief Statistical Scientist
Oracle Health Sciences
Bill’s current research focuses on statistical computing and Bayesian hierarchical models, including applications to meta-analysis and data mining. He is the inventor of the empirical Bayesian data mining algorithm known as Gamma-Poisson Shrinker (GPS) and its successor MGPS, which have been applied to the detection of safety signals in databases of spontaneous adverse drug event reports. These methods are now used within the FDA and industry. From 1996 through 2004 he was a senior member of the data mining research group at AT&T Labs. Before that, he was Chief Statistical Scientist at BBN Software Products, where he was lead statistical designer of software advisory systems for experimental design and data analysis called RS/Discover and RS/Explore. He has been on the faculties of the University of California at Berkeley, the University of Michigan, MIT, and most recently was Professor of Biostatistics and Medical Informatics at Columbia University from 1994-1996. He has authored approximately fifty papers in peer-reviewed journals and has also been an associate editor of the Journal of the American Statistical Association, Statistics in Medicine, Statistics and Computing, and the Journal of Computational and Graphical Statistics.
Schuemie MJ, Ryan PB, DuMouchel W, Suchard MA, Madigan D. Interpreting observational studies: why empirical calibration is needed to correct p-values. Stat Med. 2014 Jan 30;33(2):209-18. doi: 10.1002/sim.5925. Epub 2013 Jul 30.
Madigan D, Stang PE, Berlin JA, et al. A Systematic Statistical Approach to Evaluating Evidence from Observational Studies. Annual Review of Statistics and Its Application. 2014;1(1):11-39.
Ryan PB, Stang PE, Overhage JM, et al. A comparison of the empirical performance of methods for a risk identification system. Drug Saf. 2013 Oct;36(Suppl 1):S143-58. doi: 10.1007/s40264-013-0108-9.
DuMouchel W, Ryan PB, Schuemie MJ, Madigan D. Evaluation of disproportionality safety signaling applied to healthcare databases. Drug Saf. 2013 Oct;36(Suppl 1):S123-32. doi: 10.1007/s40264-013-0106-y.
Madigan D, Ryan PB, Schuemie M, et al. Evaluating the impact of database heterogeneity on observational study results. Am J Epidemiol. 2013 Aug 15;178(4):645-51. doi: 10.1093/aje/kwt010. Epub 2013 May 5.
Harpaz R, DuMouchel W, Shah NH, Madigan D, Ryan P, Friedman C. Novel data-mining methodologies for adverse drug event discovery and analysis. Clin Pharmacol Ther. 2012 Jun;91(6):1010-21. doi: 10.38/clpt.2012.50.