|
Abstract:
|
Semiparametric accelerated failure time (AFT) models are an attractive alternative to Cox proportional hazards models, especially when the assumption of proportional hazards is untenable. However, rank-based criteria for fitting AFT models are often non-differentiable, which poses a computational challenge in high-dimensional settings. We propose a new alternating direction method of multipliers algorithm for fitting semiparametric AFT models by minimizing a penalized rank-based objective function. Our algorithm scales well in both the number of subjects and number of predictors; and can easily accommodate a wide range of penalties. Through extensive simulation studies, we show that our software is much faster than existing methods (which can only be applied to special cases), and we show that penalized rank-based estimators 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, while weighted least squares estimators are often not.
|
