Modeling Time-Varying Effects With Large-Scale Survival Data: An Efficient Quasi-Newton Approach. Issue 3 (3rd July 2017)

• Record Type: Journal Article
• Authors: He, Kevin; Yang, Yuan; Li, Yanming; Zhu, Ji; Li, Yi
• Is Part Of: Journal of computational and graphical statistics. Volume 26:Issue 3(2017)
• Page Start: 635
• Page End: 645
• Publication Date: 2017-07-03
• Subjects: Quasi-Newton -- Spline-based methods -- Survival analysis -- Time-varying effects; Mathematical statistics -- Data processing -- Periodicals; Mathematical statistics -- Graphic methods -- Periodicals; 519.50285
• DOI: 10.1080/10618600.2016.1237364
• Languages: English
• ISSNs: 1061-8600

Abstract

ABSTRACT: Nonproportional hazards models often arise in biomedical studies, as evidenced by a recent national kidney transplant study. During the follow-up, the effects of baseline risk factors, such as patients' comorbidity conditions collected at transplantation, may vary over time. To model such dynamic changes of covariate effects, time-varying survival models have emerged as powerful tools. However, traditional methods of fitting time-varying effects survival model rely on an expansion of the original dataset in a repeated measurement format, which, even with a moderate sample size, leads to an extremely large working dataset. Consequently, the computational burden increases quickly as the sample size grows, and analyses of a large dataset such as our motivating example defy any existing statistical methods and software. We propose a novel application of quasi-Newton iteration method to model time-varying effects in survival analysis. We show that the algorithm converges superlinearly and is computationally efficient for large-scale datasets. We apply the proposed methods, via a stratified procedure, to analyze the national kidney transplant data and study the impact of potential risk factors on post-transplant survival. Supplementary materials for this article are available online.