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http://dx.doi.org/10.29220/CSAM.2018.25.6.591

Penalized variable selection for accelerated failure time models  

Park, Eunyoung (Department of Statistics, Pukyong National University)
Ha, Il Do (Department of Statistics, Pukyong National University)
Publication Information
Communications for Statistical Applications and Methods / v.25, no.6, 2018 , pp. 591-604 More about this Journal
Abstract
The accelerated failure time (AFT) model is a linear model under the log-transformation of survival time that has been introduced as a useful alternative to the proportional hazards (PH) model. In this paper we propose variable-selection procedures of fixed effects in a parametric AFT model using penalized likelihood approaches. We use three popular penalty functions, least absolute shrinkage and selection operator (LASSO), adaptive LASSO and smoothly clipped absolute deviation (SCAD). With these procedures we can select important variables and estimate the fixed effects at the same time. The performance of the proposed method is evaluated using simulation studies, including the investigation of impact of misspecifying the assumed distribution. The proposed method is illustrated with a primary biliary cirrhosis (PBC) data set.
Keywords
AFT model; LASSO; penalized likelihood; SCAD; variable selection;
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