Bayesian competing risk analysis: An application to nasopharyngeal carcinoma patients data

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 even...

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Bibliographic Details
Published in:Computational and systems oncology 2021-03, Vol.1 (1), p.n/a
Main Authors: Saroj, Rakesh Kumar, Murthy, K. Narasimha, Kumar, Mukesh, Bhattacharjee, Atanu, Patel, Kamalesh Kumar
Format: Article
Language:English
Subjects:
Cancer therapies
Chemotherapy
competing risk
Frequency distribution
MCMC
NPC
Patients
predictive density plot
predictive trace plot
Smoking
Software
Survival analysis
Throat cancer
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Summary: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.
ISSN:2689-9655
2689-9655
DOI:10.1002/cso2.1006