  Following a series of high-profile drug safety disasters in recent years,
many countries are redoubling their efforts to ensure the safety of licensed
medical products. Large-scale observational databases such as claims databases
or electronic health record systems are attracting particular attention in this
regard, but present significant methodological and computational concerns. In
this paper we show how high-performance statistical computation, including
graphics processing units, relatively inexpensive highly parallel computing
devices, can enable complex methods in large databases. We focus on
optimization and massive parallelization of cyclic coordinate descent
approaches to fit a conditioned generalized linear model involving tens of
millions of observations and thousands of predictors in a Bayesian context. We
find orders-of-magnitude improvement in overall run-time. Coordinate descent
approaches are ubiquitous in high-dimensional statistics and the algorithms we
propose open up exciting new methodological possibilities with the potential to
significantly improve drug safety.
