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Activity Number: 254 - Contributed Poster Presentations: Section on Bayesian Statistical Science
Type: Contributed
Date/Time: Monday, July 29, 2019 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #300647
Title: Bayesian Survival Analysis with Missing Covariate Values; an Application to Breast Cancer Data
Author(s): Refah Alotaibi* and Juliana Iworikumo Consul
Companies: Princess Nourah bint Abdulrahman University and Niger Delta University, Bayelsa State, Nigeria
Keywords: Hazards; survival; proportional hazards; prognosis indices; linear predictor; modelling

Abstract This research is concerned with the methodology of Bayesian survival analysis of breast cancer data with missing covariate values. The data set used in this research include survival times of breast cancer patients and a large number of covariates with missing values. We will concentrate on dealing with missing data both in the analysis of the data set and in calculations of prognostic indices for a new patient. We propose to use the Bayesian approach to inference in incorporating covariates into the model. The proportional hazard model was used to explore the relationship between the covariates and survival through a linear predictor. The analysis was implemented using R function.

Authors who are presenting talks have a * after their name.

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