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Doctoral Degrees (Statistics)

Permanent URI for this collectionhttps://hdl.handle.net/10413/7126

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Browsing Doctoral Degrees (Statistics) by Subject "Bayesian models."

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    Bayesian generalized linear mixed modeling of breast cancer data in Nigeria.
    (2017) Ogunsakin, Ropo Ebenezer.; Logue, Siaka.
    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.