Title: On the Estimation of Parametric Cause Specific Hazard Function with Bayesian Approach under Informative Priors
Montes Taurus J. Pure Appl. Math. / ISSN: 2687-4814
Article ID: MTJPAM-D-20-00025; Volume 3 / Issue 3 / Year 2021 (Special Issue), Pages 25-38
Document Type: Research Paper
Author(s): Navin Chandra a , Habbiburr Rehman b
aDepartment of Statistics, Ramanujan School of Mathematical Sciences, Pondicherry University, Puducherry-605 014, India
bDepartment of Statistics, Ramanujan School of Mathematical Sciences, Pondicherry University, Puducherry-605 014, India
Received: 24 August 2020, Accepted: 1 December 2020, Published: 25 April 2021.
Corresponding Author: Navin Chandra (Email address: firstname.lastname@example.org)
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In this article we analysed the competing risks data using cause specific hazard approach. We proposed the Cox proportional hazards regression technique of analysing the survival data in the presence of competing risks setting using baseline modified Weibull distribution. For estimating the cumulative cause specific hazard function we used maximum likelihood as well Bayesian methods of estimation. Under Bayesian scenario, we used three types of informative priors such as gamma, Weibull and log-normal for baseline parameters and standard normal prior for regression parameters. The Comparison of Bayes estimates is made based on two different loss functions like, squared error and LINEX loss functions. Simulation study shows the appropriate convergence and identifiability of the proposed model. The bladder cancer data is utilized for the validation of proposed study.
Keywords: Competing risks, Modified Weibull distribution, Informative priors, Squared error loss function, LINEX loss function, Makov Chain Monte Carlo simulationReferences:
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