289 – SBSS Student Paper Competition Winners
Bayesian Survival Analysis via Transform-Both-Sides Model
Jianchang Lin
Millennium: The Takeda Oncology Company
Stuart Lipsitz
Harvard Medical School
Adriano Polpo
Federal University of Sao Carlos
Debajyoti Sinha
Florida State University
We present a novel semiparametric survival model with log-linear median regression function. This wide class of models is an useful alternative to the popular Cox (1972) model and linear transformation models (Cheng et al., 1995). Compared to existing semiparametric models, our models have many important practical advantages, including interpretation of the regression parameters via the median and the ability to address heteroscedasticity. We demonstrate that our modeling techniques facilitate 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 reanalysis of a small-cell lung cancer study. Results of our simulation study provide further guidance regarding appropriate modeling in practice.
