Abstract
ABSTRACT Survival analysis is an important analytic method in the social and medical sciences. Also known under the name time-to-event analysis, this method provides parameter estimation and model fitting commonly conducted via maximum-likelihood. Bayesian survival analysis offers multiple advantages over the frequentist approach for measurement practitioners, however, computational difficulties have mitigated interest in Bayesian survival models. This paper shows that Bayesian survival models can be fitted in a straightforward manner via the probabilistic programming language Stan, which offers full Bayesian inference through Hamiltonian Monte Carlo algorithms. Illustrations show the benefits for measurement practitioners in the social and medical sciences.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Measurement: Interdisciplinary Research and Perspectives
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
