Scalable proximal methods for cause-specific hazard modeling with time-varying coefficients.

ABSTRACT: Survival modeling with time-varying coefficients has proven useful in analyzing time-to-event data with one or more distinct failure types. When studying the cause-specific etiology of breast and prostate cancers using the large-scale data from the Surveillance, Epidemiology, and End Results (SEER) Program, we encountered two major challenges that existing methods for estimating time-varying coefficients cannot tackle. First, these methods, dependent on expanding the original data in a repeated measurement format, result in formidable time and memory consumption as the sample size escalates to over one million. In this case, even a well-configured workstation cannot accommodate their implementations. Second, when the large-scale data under analysis include binary predictors with near-zero

SUBMITTER: Wu W 

PROVIDER: S-EPMC9201734 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature