Background: Survival analysis is a statistical method for modeling the probability that a subset of a given population will survive past a certain time. In the context of cancer, this probability would represent a recurrence of tumor, or remission (i.e. being disease-free). This study seeks to compare the traditional frequentist approach and the Bayesian approach to survival analysis in estimating, and the predictors of prostate cancer (CaP) survivorship. Prostate cancer starts when healthy cells in the prostate gland change and grow out of place, forming a mass called a tumor.
Method: A retrospective analytical study design was employed, through the extraction of case files of patients diagnosed and treated for CaP from January 2010 to December 2017 at UCH, Ibadan. The extracted data were further divided into two cohorts (2010 - 2014) and (2015 - 2017). A proforma was used for extraction which includes the following sections; socio-demographic, clinical/pathological characteristics, date of diagnosis, date last seen, and treatment received. Descriptive statistics were used to describe these characteristics. The survival probability was determined by the KM survival method. Cox proportional hazard (CPH), Weibull AFT, and Bayesian Weibull AFT (using normal prior distribution) models were used to determine predictors of survivorship.
Results: The average age of the patients was 72 years, with a peak incidence of CaP among those aged 70 79 years. Most patients 87.3% were diagnosed at stage IV, with many having metastasis to the spine. Among the patients, 33.6% received chemotherapy and surgery. Patients from Northcentral had the highest median survival (MS) time in the (2015 - 2017) cohort. The overall MS time for the (2010 - 2014) cohort was 2.9 months, and 3.3 months for the (2015 - 2017) cohort while the overall MS time for the study was 3.2 months. Patients treated with chemotherapy and surgery in both cohorts experienced delayed remission. The Weibull AFT model found that patients with a moderately differentiated Gleason experienced a 50% increase in time (TR = 0.5; 95%CI: 0.3 0.9) to remission. Patients, with a poorly differentiated Gleason, experienced a 70% decrease in time (aTR = 1.7; 95%CI: 1.0 - 2.7) to remission. The Bayesian AFT model also found delay in time to remission for patients with moderately differentiated Gleason (TR = 0.6; 95%CrI: 0.3 0.9), and those treated with Chemotherapy and Surgery (aTR = 3.3; 95%CrI: 2.6 4.4). The Bayesian model showed that age, south-south, north-central, no family history of CaP, moderately and poorly differentiated Gleason and treatment with Chemotherapy, Radiotherapy, and Surgery, Chemotherapy, and Surgery to significantly delay time to remission.
Conclusion: This study found that in considering predictors of survivorship a host of factors should be considered, particularly age, location, marital status, occupation, stage, method of diagnosis, Gleason group, site, and treatment received. In terms of approaches to survival analysis, greater emphasis should be given to the Bayesian approach, as observed in this study, the Bayesian approach extracted more significant predictors of survivorship than the CPH and Weibull AFT models and besides, it is more suitable for studies with fewer observations.