Stability selection for lasso, ridge and elastic net implemented with AFT models

• Published in: Statistical applications in genetics and molecular biology Vol. 18; no. 5
• Main Authors: Khan, Md Hasinur Rahaman, Bhadra, Anamika, Howlader, Tamanna
• Format: Journal Article
• Language: English
• Published: Germany De Gruyter 01.10.2019; Walter de Gruyter GmbH
• Subjects: AFT model; Algorithms; Breast Neoplasms - genetics; Breast Neoplasms - metabolism; Breast Neoplasms - mortality; Breast Neoplasms - pathology; Collinearity; Computer Simulation; Elastic net; Failure times; Female; Humans; Lasso; Linear Models; Lymphoma, B-Cell - genetics; Lymphoma, B-Cell - metabolism; Lymphoma, B-Cell - mortality; Neoplasm Metastasis; Probability; Ridge; Stability; Stability selection; Stability selection; Elastic net; AFT model; Lasso; Ridge
• ISSN: 1544-6115; 2194-6302; 1544-6115
• DOI: 10.1515/sagmb-2017-0001

The instability in the selection of models is a major concern with data sets containing a large number of covariates. We focus on stability selection which is used as a technique to improve variable selection performance for a range of selection methods, based on aggregating the results of applying a selection procedure to sub-samples of the data where the observations are subject to right censoring. The accelerated failure time (AFT) models have proved useful in many contexts including the heavy censoring (as for example in cancer survival) and the high dimensionality (as for example in micro-array data). We implement the stability selection approach using three variable selection techniques—Lasso, ridge regression, and elastic net applied to censored data using AFT models. We compare the performances of these regularized techniques with and without stability selection approaches with simulation studies and two real data examples–a breast cancer data and a diffuse large B-cell lymphoma data. The results suggest that stability selection gives always stable scenario about the selection of variables and that as the dimension of data increases the performance of methods with stability selection also improves compared to methods without stability selection irrespective of the collinearity between the covariates.