title: Bayesian variable selection in the accelerated failure time model with an application to the surveillance, epidemiology, and end results breast cancer data.
abstract: Accelerated failure time model is a popular model to analyze censored time-to-event data. Analysis of this model without assuming any parametric distribution for the model error is challenging, and the model complexity is enhanced in the presence of large number of covariates. We developed a nonparametric Bayesian method for regularized estimation of the regression parameters in a flexible accelerated failure time model. The novelties of our method lie in modeling the error distribution of the accelerated failure time nonparametrically, modeling the variance as a function of the mean, and adopting a variable selection technique in modeling the mean. The proposed method allowed for identifying a set of important regression parameters, estimating survival probabilities, and constructing credible intervals of the survival probabilities. We evaluated operating characteristics of the proposed method via simulation studies. Finally, we apply our new comprehensive method to analyze the motivating breast cancer data from the Surveillance, Epidemiology, and End Results Program, and estimate the five-year survival probabilities for women included in the Surveillance, Epidemiology, and End Results database who were diagnosed with breast cancer between 1990 and 2000.
authors.affiliation: 1 Department of Statistics, University of Chicago, Chicago, IL, USA.
authors.forename: Zhen
authors.initials: Z
authors.lastname: Zhang
authors.affiliation: 2 Department of Statistics, Texas A&M University, College Station, TX, USA.
authors.forename: Samiran
authors.initials: S
authors.lastname: Sinha
authors.affiliation: 3 Department of Statistics and Probability, Michigan State University, East Lansing, MI, USA.
authors.forename: Tapabrata
authors.initials: T
authors.lastname: Maiti
authors.affiliation: 4 Texas A&M Health Science Center, School of Rural Public Health, College Station, TX, USA.
authors.forename: Eva
authors.initials: E
authors.lastname: Shipp
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001499:Bayes Theorem; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000473:pathology*; D005260:Female; D006801:Humans; D008875:Middle Aged; D015233:Models, Statistical; D009010:Monte Carlo Method; D011159:Population Surveillance; D011379:Prognosis; D018426:SEER Program / Q000706:statistics & numerical data; D016019:Survival Analysis; D055815:Young Adult
keywords: Accelerated failure time; Bayesian LASSO; Dirichlet process prior; Markov chain Monte Carlo; Prognostic factors; Survival probability