title: Enhancing the Lasso Approach for Developing a Survival Prediction Model Based on Gene Expression Data.
abstract: In the past decade, researchers in oncology have sought to develop survival prediction models using gene expression data. The least absolute shrinkage and selection operator (lasso) has been widely used to select genes that truly correlated with a patient's survival. The lasso selects genes for prediction by shrinking a large number of coefficients of the candidate genes towards zero based on a tuning parameter that is often determined by a cross-validation (CV). However, this method can pass over (or fail to identify) true positive genes (i.e., it identifies false negatives) in certain instances, because the lasso tends to favor the development of a simple prediction model. Here, we attempt to monitor the identification of false negatives by developing a method for estimating the number of true positive (TP) genes for a series of values of a tuning parameter that assumes a mixture distribution for the lasso estimates. Using our developed method, we performed a simulation study to examine its precision in estimating the number of TP genes. Additionally, we applied our method to a real gene expression dataset and found that it was able to identify genes correlated with survival that a CV method was unable to detect.
authors.affiliation: Department of Management Science, Graduate School of Engineering, Tokyo University of Science, 1-3 Kagurazaka, Shinjuku-ku, Tokyo 162-8601, Japan.
authors.forename: Shuhei
authors.initials: S
authors.lastname: Kaneko
authors.affiliation: Biostatistics and Bioinformatics Section, Center for Advanced Medicine and Clinical Research, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya 466-8560, Japan.
authors.forename: Akihiro
authors.initials: A
authors.lastname: Hirakawa
authors.affiliation: Department of Management Science, Graduate School of Engineering, Tokyo University of Science, 1-3 Kagurazaka, Shinjuku-ku, Tokyo 162-8601, Japan.
authors.forename: Chikuma
authors.initials: C
authors.lastname: Hamada
mesh_terms: D000465:Algorithms; D019295:Computational Biology; D003198:Computer Simulation; D005188:False Negative Reactions; D005189:False Positive Reactions; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D008495:Medical Oncology; D015233:Models, Statistical; D009369:Neoplasms / Q000235:genetics* / Q000378:metabolism*; D011379:Prognosis; D016016:Proportional Hazards Models; D012044:Regression Analysis; D015203:Reproducibility of Results