High-Dimensional Additive Hazards Regression for Oral Squamous Cell Carcinoma Using Microarray Data: A Comparative Study

Abstract

Microarray technology results in high-dimensional and low-sample size data sets. Therefore, fitting sparse models is substantial because only a small number of influential genes can reliably be identified. A number of variable selection approaches have been proposed for high-dimensional time-to-event data based on Cox proportional hazards where censoring is present. The present study applied three sparse variable selection techniques of Lasso, smoothly clipped absolute deviation and the smooth integration of counting, and absolute deviation for gene expression survival time data using the additive risk model which is adopted when the absolute effects of multiple predictors on the hazard function are of interest. The performances of used techniques were evaluated by time dependent ROC curve and bootstrap .632+ prediction error curves. The selected genes by all methods were highly significant (P < 0.001). The Lasso showed maximum median of area under ROC curve over time (0.95) and smoothly clipped absolute deviation showed the lowest prediction error (0.105). It was observed that the selected genes by all methods improved the prediction of purely clinical model indicating the valuable information containing in the microarray features. So it was concluded that used approaches can satisfactorily predict survival based on selected gene expression measurements.

Identification of Prognostic Biomarkers for Breast Cancer Metastasis Using Penalized Additive Hazards Regression Model

“… 14 Penalized additive hazards model has been also developed for variables selection in the context of survival analysis and has been applied for selecting gene profiles related to various cancers. 15 , 16 Nevertheless, to our knowledge, there is no study that analyzes molecular data with survival outcome in BC patients to create prognostic models for this disease using additive risks model. The aim of the present study was to focus on utilization of penalized additive hazards regression model for analyzing high-dimensional time-to-metastasis data, to create a diagnostic model to predict survival time in BC patients, and to determine genes associated with time-to-metastasis.…”