Bayesian generalized linear mixed modeling of breast cancer data in Nigeria.
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Date
2017
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Abstract
Breast cancer is the world’s most prevalent type of cancer among women. Statistics
indicate that breast cancer alone accounted for 37% out of all the cases of cancer diagnosed
in Nigeria in 2012. Data used in this study are extracted from patient records,
commonly called hospital-based records, and identified key socio-demographic and
biological risk factors of breast cancer. Researchers sometimes ignore the hierarchical
structure of the data and the disease when analyzing data. Doing so may lead to
biased parameter estimates and larger standard error. That is why the analyses undertaken
in this study included the multilevel structure of cancer diagnosis, types,
and medication through a Generalized Linear Mixed Model (GLMM) which consider
both fixed and random effects (level 1 and 2). In addition to the classical statistics
approach, this study incorporates the Bayesian GLMM approach as well as some
bootstrapping techniques. All the analyses are done using R or SAS for the classical
statistics approaches, and WinBUGS for the Bayesian approach. The Bayesian analyses
were strengthened by advanced analyses of convergence and autocorrelation
checks, and other Markov Chain assumptions using the CODA and BOA packages.
The findings reveal that Bayesian techniques provide more comprehensive results,
given that Bayesian analysis is a more statistically strong technique. The Bayesian
methods appeared more robust than the classical and bootstrapping techniques in
analyzing breast cancer data in Western Nigeria.
The results identified age at diagnosis, educational status, grade tumor, and breast
cancer type as prognostic factors of breast cancer.
Description
Doctor of Philosophy in Statistics, University of KwaZulu-Natal, Westville, 2017.