Hybrid Bayesian Estimation in the additive hazards model

TL;DR

This paper introduces a hybrid Bayesian estimation approach for the semiparametric Additive Hazards Model in survival analysis, addressing challenges in Bayesian inference under right-censoring and providing closed-form estimators.

Contribution

It develops a novel hybrid Bayesian method that combines estimating equations with tractable priors, enabling closed-form posterior estimators for the AHM.

Findings

Closed-form posterior estimators obtained

Method validated with simulations and real data

Addresses Bayesian estimation challenges in survival analysis

Abstract

Hereby we propose a Bayesian method of estimation for the semiparametric Additive Hazards Model (AHM) from Survival Analysis under right-censoring. With this aim, we review the AHM revisiting the likelihood function, so as to comment on the challenges posed by Bayesian estimation from the full likelihood. Through an algorithmic reformulation of that likelihood, we present an alternative method based on a hybrid Bayesian treatment that exploits Lin and Ying (1994) estimating equation approach and which chooses tractable priors for the parameters. We obtain the estimators from the posterior distributions in closed form, we perform a small simulation experiment, and lastly, we illustrate our method with the classical Nickels Miners dataset and a brief simulation experiment.