Bayesian Methods for Ranking the Severity of Apnea among Patients

Abstract

Problem statement: Studies on apnea patients are often carried out based on data obtained from the sleep study. This data is quite scarce since high cost is required for conducting the study. Bayesian method is particularly suitable for analyzing limited data as it allows for updating of information by combining the current information with the prior belief. Approach: In this study we demonstrated the use of Bayesian methods to rank the severity of apnea for 14 patients, based on the posterior mean of the rate of occurrence of apnea. Results: The results indicated from the comparison using three different prior distribution for the underlying rate of occurrence of apnea, that is improper, gamma and log-normal priors, the ranking of patients in terms of severity of apnea are the same, regardless of the choice for the prior distributions. Conclusion: In conclusion the model fitting was found to be slightly better when based on gamma prior.