Massive Parallelization of Massive Sample-size Survival Analysis

TL;DR

This paper introduces GPU-based parallel algorithms to significantly accelerate large-scale survival analysis models, enabling efficient processing of millions of patients in observational health studies.

Contribution

It develops and applies GPU-accelerated parallel scan algorithms for Cox and Fine-Gray models, improving computational speed over traditional CPU methods.

Findings

GPUs accelerate model fitting by orders of magnitude.

Enables large-scale studies with millions of patients.

Implementation available in open-source R package Cyclops.

Abstract

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).