  Large-scale observational health databases are increasingly popular for
conducting comparative effectiveness and safety studies of medical products.
However, increasing number of patients poses computational challenges when
fitting survival regression models in such studies. In this paper, we use
graphics processing units (GPUs) to parallelize the computational bottlenecks
of massive sample-size survival analyses. Specifically, we develop and apply
time- and memory-efficient single-pass parallel scan algorithms for Cox
proportional hazards models and forward-backward parallel scan algorithms for
Fine-Gray models for analysis with and without a competing risk using a cyclic
coordinate descent optimization approach. We demonstrate that GPUs accelerate
the computation of fitting these complex models in large databases by orders of
magnitude as compared to traditional multi-core CPU parallelism. Our
implementation enables efficient large-scale observational studies involving
millions of patients and thousands of patient characteristics. The above
implementation is available in the open-source R package Cyclops (Suchard et
al., 2013).
