Bayesian assessment of times to diagnosis in breast cancer screening

Carmen Armero, Antonio Lopez-Quilez and Rut Lopez-Sanchez

Journal of Applied Statistics, 2008, vol. 35, issue 9, 997-1009

Abstract: Breast cancer is one of the diseases with the most profound impact on health in developed countries and mammography is the most popular method for detecting breast cancer at a very early stage. This paper focuses on the waiting period from a positive mammogram until a confirmatory diagnosis is carried out in hospital. Generalized linear mixed models are used to perform the statistical analysis, always within the Bayesian reasoning. Markov chain Monte Carlo algorithms are applied for estimation by simulating the posterior distribution of the parameters and hyperparameters of the model through the free software WinBUGS.

Keywords: Bayesian statistics; breast cancer screening program; generalized linear mixed models; Markov Chain Monte Carlo (search for similar items in EconPapers)
Date: 2008
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DOI: 10.1080/02664760802191397

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