DeepAI
Log In Sign Up

Scalable Algorithms for Large Competing Risks Data

11/07/2019
by   Eric S. Kawaguchi, et al.
0

This paper develops two orthogonal contributions to scalable sparse regression for competing risks time-to-event data. First, we study and accelerate the broken adaptive ridge method (BAR), an ℓ_0-based iteratively reweighted ℓ_2-penalization algorithm that achieves sparsity in its limit, in the context of the Fine-Gray (1999) proportional subdistributional hazards (PSH) model. In particular, we derive a new algorithm for BAR regression, named cycBAR, that performs cyclic update of each coordinate using an explicit thresholding formula. The new cycBAR algorithm effectively avoids fitting multiple reweighted ℓ_2-penalizations and thus yields impressive speedups over the original BAR algorithm. Second, we address a pivotal computational issue related to fitting the PSH model. Specifically, the computation costs of the log-pseudo likelihood and its derivatives for PSH model grow at the rate of O(n^2) with the sample size n in current implementations. We propose a novel forward-backward scan algorithm that reduces the computation costs to O(n). The proposed method applies to both unpenalized and penalized estimation for the PSH model and has exhibited drastic speedups over current implementations. Finally, combining the two algorithms can yields >1,000 fold speedups over the original BAR algorithm. Illustrations of the impressive scalability of our proposed algorithm for large competing risks data are given using both simulations and a United States Renal Data System data.

READ FULL TEXT

page 1

page 2

page 3

page 4

05/17/2019

A Fast and Scalable Implementation Method for Competing Risks Data with the R Package fastcmprsk

Advancements in medical informatics tools and high-throughput biological...
12/02/2017

Scalable Sparse Cox's Regression for Large-Scale Survival Data via Broken Adaptive Ridge

This paper develops a new sparse Cox regression method for high-dimensio...
04/18/2022

Massive Parallelization of Massive Sample-size Survival Analysis

Large-scale observational health databases are increasingly popular for ...
10/05/2020

The use of restricted mean time lost under competing risks data

Background: Under competing risks, the commonly used sub-distribution ha...
04/02/2018

A Fast Divide-and-Conquer Sparse Cox Regression

We propose a computationally and statistically efficient divide-and-conq...