On Bayesian Analysis Of The Exponential Survival Time Assuming The Exponential Censor Time

Abstract

Random censoring is a type of right censoring in time-to-event studies. The exponential survival time with an exponential censor time is focused to derive classically and the Bayes estimators. The uniform and the Inverted Gamma priors are assumed to carry out the Bayesian analysis. The posterior predictive distribution is derived and the equations required for the construction of predictive intervals are developed. The construction of the credible intervals and that of the Highest Posterior Density (HPD) intervals is elaborated theoretically as well as conducted numerically. A comprehensive simulation study assuming various parameter points and sample sizes are conducted to highlight the properties and comparison of the estimates.