Bayesian competing risk analysis: An application to nasopharyngeal carcinoma patients data. Issue 1 (3rd January 2021)

• Record Type: Journal Article
• Authors: Saroj, Rakesh Kumar; Murthy, K. Narasimha; Kumar, Mukesh; Bhattacharjee, Atanu; Patel, Kamalesh Kumar
• Is Part Of: Computational and systems oncology. Volume 1:Issue 1(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-01-03
• Subjects: competing risk -- MCMC -- NPC -- predictive density plot -- predictive trace plot; Oncology -- Data processing -- Periodicals; Oncology -- Periodicals; Systems biology -- Periodicals; Medical Oncology; Neoplasms; Electronic journals; Periodicals; 616.994
• DOI: 10.1002/cso2.1006
• Languages: English
• ISSNs: 2689-9655

Abstract

Abstract: Background: The Cox proportional hazard (CPH) model is normally used to study the death event data. The presence of competing risk (CR) is often encountered in health data, hence it becomes difficult to manage time to event data in clinical study. Bayesian approach is considered to manage the CR events in clinical data. Objectives: The objective of study is to find the predictors associated with overall survival of nasopharyngeal carcinoma (NPC) patients. Further, our purpose is to use a Bayesian model that can analyze time to event data in the presence of CR. Methods: Total 245 patients with NPC were taken (https://www.ncbi.nlm.nih.gov/geo/ ). The sociodemographic and clinical variables were considered for analysis purposes. R software and openBUGS were used to overcome the computational problems of CPH and Bayesian models. The Markov chain Monte Carlo (MCMC) method was used to compute the regression coefficients of Bayesian model. Results: The study shows that among NPC patients, the covariates chemotherapy, smoking, N‐stage, and tumor site are associated with the higher risk for the deaths occurring in the cancer patients. The posterior mean estimates of proposed Bayesian model for significant factors have been obtained. The posterior mean and standard deviation estimates help to improve the survival of patients in the presence of CR. Conclusions: It is very difficult to use the CR model with Bayesian approach in health research for nonstatistical researcher due to lack of information. This paper is dedicated to the application of Bayesian approach for CR analysis on NPC data.