Abstract
In the article, the performance of Bayesian parametric survival models (Weibull, exponential, log-normal and log-logistic) by using Monte Carlo simulation was empirically compared while varying the informative priors and the sample sizes. We simulated the generated data by running for each of Weibull, exponential, log-normal and log-logistic survival models under varying informative priors and sample sizes using our simulation algorithm. For each situation, 1000 simulations were performed. Models with proper informative prior showed a good performance with too little bias. It was found out that bias of models increased while priors were becoming distant from reliability in all sample sizes. According to results obtained from simulation study, researchers should avoid assessment of data by using only one parametric survival model in future studies. We suggest that data should be better explored and processed by high performance modelling methods. Especially, quality of prior information to update knowledge about the parameters was a very important.
Published Version
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