Abstract
For survival data with a large number of explanatory variables, lasso penalized Cox regression is a popular regularization strategy. However, a penalized Cox model may not always provide the best fit to data and can be difficult to estimate in high dimension because of its intrinsic nonlinearity. The semiparametric additive hazards model is a flexible alternative which is a natural survival analogue of the standard linear regression model. Building on this analogy, we develop a cyclic coordinate descent algorithm for fitting the lasso and elastic net penalized additive hazards model. The algorithm requires no nonlinear optimization steps and offers excellent performance and stability. An implementation is available in the R package ahaz. We demonstrate this implementation in a small timing study and in an application to real data.
Highlights
With the increasing interest in high-throughput biomarker research, there is a growing need for simple and efficient statistical methods for relating a survival time endpoint to a large number of explanatory variables
Many standard survival regression models have been subjected to various penalization strategies (Li, 2008), yet the Cox proportional hazards model continues to serve as a reference model and the main target of theoretical, algorithmic, and applied research on penalized survival regression
We develop a cyclic coordinate descent algorithm for the elastic net penalized variant of a flexible but less well-known alternative to the Cox model, the so-called semiparametric additive hazards model (Lin and Ying, 1994; McKeague and Sasieni, 1994)
Summary
With the increasing interest in high-throughput biomarker research, there is a growing need for simple and efficient statistical methods for relating a survival time endpoint to a large number of explanatory variables. We develop a cyclic coordinate descent algorithm for the elastic net penalized variant of a flexible but less well-known alternative to the Cox model, the so-called semiparametric additive hazards model (Lin and Ying, 1994; McKeague and Sasieni, 1994). This model asserts a hazard function given by the sum of some baseline hazard function and a regression function of the explanatory variables. The algorithm has been implemented in C to interface with the R-package ahaz (Gorst-Rasmussen, 2011), and we provide examples of its usage and performance on simulated and real data
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
