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Activity Number: 134 - Bayesian Modeling
Type: Contributed
Date/Time: Monday, August 9, 2021 : 1:30 PM to 3:20 PM
Sponsor: Section on Statistical Computing
Abstract #318157
Title: Efficient Bayesian Nonparametric Hazard Regression
Author(s): Matthias Kaeding*
Companies: RWI - Leibniz Institute for Economic Research
Keywords: Bayesian survival analysis; Nonparametric modeling; Penalized spline; Restricted mean survival time
Abstract:

We model the log-cumulative baseline hazard for the Cox model via Bayesian, monotonic P-splines. This approach permits fast computation, accounting for arbitrary censorship and the inclusion of nonparametric effects. We leverage the computational efficiency to simplify effect interpretation for metric and non-metric variables by combining the restricted mean survival time approach with partial dependence plots. This allows effect interpretation in terms of survival times. Monte Carlo simulations indicate that the proposed methods work well. We illustrate our approach using a large data set of real estate data advertisements.


Authors who are presenting talks have a * after their name.

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