Coordinate Descent Methods for the Penalized Semiparametric Additive Hazards Model

By Anders Gorst-rasmussen, Thomas H. Scheike. 2012

64 citations recorded by the source corpus.

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.

Subfields: Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/2198287de7e60965f7dd6f28bd79a956409f7b4e

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In Statistics and Probability

Citations in the corpus

Independent screening for single‐index hazard rate models with ultrahigh dimensional features (2011, methodology): “OS SCAD can be implemented efficiently by using, for example, co-ordinate descent methods for fitting the lasso (Gorst-Rasmussen and Scheike, 2012; Friedman et al., 2007).”
Elastic Net Regularization Paths for All Generalized Linear Models (2021, methodology): “(Gorst-Rasmussen and Scheike 2012) can fit semiparametric additive hazards models (which include the Cox model as a special case) with the lasso penalty but not the elastic net penalty.”