title: Nonidentical twins: Comparison of frequentist and Bayesian lasso for Cox models.
abstract: One important task in translational cancer research is the search for new prognostic biomarkers to improve survival prognosis for patients. The use of high-throughput technologies allows simultaneous measurement of genome-wide gene expression or other genomic data for all patients in a clinical trial. Penalized likelihood methods such as lasso regression can be applied to such high-dimensional data, where the number of (genomic) covariables is usually much larger than the sample size. There is a connection between the lasso and the Bayesian regression model with independent Laplace priors on the regression parameters, and understanding this connection has been useful for understanding the properties of lasso estimates in linear models (e.g. Park and Casella, 2008). In this paper, we study the lasso in the frequentist and Bayesian frameworks in the context of Cox models. For the Bayesian lasso we extend the approach by Lee et al. (2011). In particular, we impose the lasso penalty only on the genome features, but not on relevant clinical covariates, to allow the mandatory inclusion of important established factors. We investigate the models in high- and low-dimensional simulation settings and in an application to chronic lymphocytic leukemia.
authors.affiliation: Division of Biostatistics, German Cancer Research Center, Heidelberg 69120, Germany.|Oslo Center for Biostatistics and Epidemiology, Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, PO Box 1122 Blindern, 0317 Oslo, Norway.
authors.forename: Manuela
authors.initials: M
authors.lastname: Zucknick
authors.affiliation: Division of Biostatistics, German Cancer Research Center, Heidelberg 69120, Germany.
authors.forename: Maral
authors.initials: M
authors.lastname: Saadati
authors.affiliation: Division of Biostatistics, German Cancer Research Center, Heidelberg 69120, Germany.
authors.forename: Axel
authors.initials: A
authors.lastname: Benner
mesh_terms: D001499:Bayes Theorem; D056808:Biostatistics / Q000379:methods*; D002986:Clinical Trials as Topic; D020869:Gene Expression Profiling; D006801:Humans; D015451:Leukemia, Lymphocytic, Chronic, B-Cell / Q000175:diagnosis / Q000235:genetics; D011379:Prognosis; D016016:Proportional Hazards Models
keywords: Shrinkage; Survival; ℓ1-Penalized likelihood