title: Semiparametric Bayesian survival analysis using models with log-linear median.
abstract: We present a novel semiparametric survival model with a log-linear median regression function. As a useful alternative to existing semiparametric models, our large model class has many important practical advantages, including interpretation of the regression parameters via the median and the ability to address heteroscedasticity. We demonstrate that our modeling technique facilitates the ease of prior elicitation and computation for both parametric and semiparametric Bayesian analysis of survival data. We illustrate the advantages of our modeling, as well as model diagnostics, via a reanalysis of a small-cell lung cancer study. Results of our simulation study provide further support for our model in practice.
authors.affiliation: Department of Statistics, Florida State University, Tallahassee, FL 32306, USA. jlin@stat.fsu.edu
authors.forename: Jianchang
authors.initials: J
authors.lastname: Lin
authors.forename: Debajyoti
authors.initials: D
authors.lastname: Sinha
authors.forename: Stuart
authors.initials: S
authors.lastname: Lipsitz
authors.forename: Adriano
authors.initials: A
authors.lastname: Polpo
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004812:Epidemiologic Methods; D015233:Models, Statistical; D016019:Survival Analysis; D015996:Survival Rate