Variable selection for accelerated lifetime models with synthesized estimation techniques

Abstract

We develop variable selection approaches for accelerated failure time models, consisting of a group of algorithms based on a synthesis of two widely used techniques in the area of variable selection for survival analysis-the Buckley-James method and the Dantzig selector. Two algorithms are based on proposed modified Buckley-James estimating methods that are designed for high-dimensional censored data. Another two algorithms are based on a two-stage weighted Dantzig selector method where weights are obtained from the two proposed synthesis-based algorithms. The methods are easy to understand and they perform estimation and variable selection simultaneously. Furthermore, they can deal with collinearity among the covariates. We conducted several simulation studies and one empirical analysis with a microarray dataset; these studies demonstrated satisfactory variable selection performance. In addition, the microarray data analysis shows the methods performing similarly to three other correlation-based greedy variable selection techniques in the literature-sure independence screening, tilted correlation screening (TCS), and partial correlation (PC) simple. This empirical study also found that the sure independence screening technique considerably improves the performance of most of the proposed methods.

Approximate Laplace Approximations for Scalable Model Selection

“…We compared the ALA results obtained under gMOM and gZellner priors to the LA under the gZellner prior, the semi‐parametric AFT model with LASSO penalties of Rahaman‐Khan and Shaw (2019) (AFT‐LASSO), and to the Cox model with LASSO penalties of Simon et al. (2011) (Cox‐LASSO).…”

Buckley–James estimation of generalized additive accelerated lifetime model with ultrahigh‐dimensional data

“…Recently, the variable screening methods for survival models have been greatly enhanced, such as the Kolmogorov-Smirnov-based survival data variable screening method proposed by Liu et al [22], the joint screening method for right-censored ultrahigh-dimensional data proposed by Liu et al [23], and feature screening method for semi-competitive risk outcomes proposed by Peng and Xiang [24]. In addition, there are new advances in variable selection for survival data, for example, Khan and Shaw [25] provided an effective variable selection method for accelerated failure models based on a two-stage weighted Dantzig selector. Liu et al [26] proposed a model pursuit variable selection method for additive accelerated failure models.…”

Data science in HIV : statistical approaches for therapeutic HIV vaccine data

“…Finally, it is also important to mention a work which is also related to the use of elastic net in AFT models. Khan & Shaw (2019) proposed four variable selection algorithms based on the Bucley-James and the Dantzig selector methods.…”