Scalable algorithms for semiparametric accelerated failure time models in high dimensions

TL;DR

This paper introduces a scalable algorithm for fitting high-dimensional semiparametric AFT models using penalized rank-based loss, improving computational efficiency and tuning parameter selection, with demonstrated superior performance over existing methods.

Contribution

The authors develop a new alternating direction method of multipliers algorithm for high-dimensional semiparametric AFT models, accommodating various penalties and improving computational speed and tuning.

Findings

Algorithm is significantly faster than existing methods.

Penalized rank-based estimators outperform weighted least squares.

Rank-based estimators are competitive with proportional hazards models.

Abstract

Semiparametric accelerated failure time (AFT) models are a useful alternative to Cox proportional hazards models, especially when the assumption of constant hazard ratios is untenable. However, rank-based criteria for fitting AFT models are often non-differentiable, which poses a computational challenge in high-dimensional settings. In this article, we propose a new alternating direction method of multipliers algorithm for fitting semiparametric AFT models by minimizing a penalized rank-based loss function. Our algorithm scales well in both the number of subjects and number of predictors; and can easily accommodate a wide range of popular penalties. To improve the selection of tuning parameters, we propose a new criterion which avoids some common problems in cross-validation with censored responses. Through extensive simulation studies, we show that our algorithm and software is much faster than existing methods (which can only be applied to special cases), and we show that estimators which minimize a penalized rank-based criterion often outperform alternative estimators which minimize penalized weighted least squares criteria. Application to nine cancer datasets further demonstrates that rank-based estimators of semiparametric AFT models are competitive with estimators assuming proportional hazards model in high-dimensional settings, whereas weighted least squares estimators are often not. A software package implementing the algorithm, along with a set of auxiliary functions, is available for download at github.com/ajmolstad/penAFT.