Bayesian Approach in Modeling Prostate Cancer

Introduction: Prostate cancer is an emerging health problem in Sub-Saharan Africa. It is often diagnosed at an advanced stage due to lack of access to screening and diagnostic facilities. Methods: This study therefore aimed to model the effects of risk factors on the outcome of prostate cancer screening using Generalized Bayesian ordinal logistic regression with random effects then compare the results obtained with the model without random effects. The study further used Mean Squared Errors to establish if the estimates for the two models were different. Results: The findings in this study indicate that aged individuals have high chances of having prostate cancer at the early, late or advanced stage. The individual with traces of family history and hereditary breast & ovarian cancer syndrome are also most likely to be in late or advanced stage of prostate cancer. Conclusion: From the findings aged individuals, having traces of family history and individuals with hereditary breast & ovarian cancer history, should make sure they understand all symptoms of prostate cancer so that incase of any signs they immediately seek for screening services. In addition, the Ministry of Health should create awareness training and increase screening facilities, this will also encourage for early screening and detection of prostate cancer. The models with presence of random effects were considered best since they had lowest Widely Applicable Information Criterion values in each category.