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    Bayesian evaluation of breast cancer screening using data from two studies.

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    Authors
    Myles, Jonathan P
    Nixon, Richard M
    Duffy, Stephen W
    Tabar, Laszlo
    Boggis, C
    Evans, D Gareth R
    Shenton, Andrew
    Howell, Anthony
    Affiliation
    Department of Mathematics, Statistics and Epidemiology, Cancer Research UK, London, UK. jonathan.myles@cancer.org.uk
    Issue Date
    2003-05-30
    
    Metadata
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    Abstract
    The mean sojourn time (the duration of the period during which a cancer is symptom free but potentially detectable by screening) and the screening sensitivity (the probability that a screen applied to a cancer in the preclinical screen detectable period will result in a positive diagnosis) are two important features of a cancer screening programme. Little data from any single study are available on the potential effectiveness of mammographic screening for breast cancer in women with a family history of the disease, despite this being an important public health issue. We develop a method of estimation, from two separate studies, of the two parameters, assuming that transition from no disease to preclinical screen detectable disease, and from preclinical disease to clinical disease, are Poisson processes. Estimation is performed by a Markov chain Monte Carlo algorithm. The method is applied to the synthesis of two studies of mammographic screening in women with a family history of breast cancer, one in Manchester and one in Kopparberg, Sweden.
    Citation
    Bayesian evaluation of breast cancer screening using data from two studies. 2003, 22 (10):1661-74 Stat Med
    Journal
    Statistics in Medicine
    URI
    http://hdl.handle.net/10541/78242
    DOI
    10.1002/sim.1365
    PubMed ID
    12720303
    Type
    Article
    Language
    en
    ISSN
    0277-6715
    ae974a485f413a2113503eed53cd6c53
    10.1002/sim.1365
    Scopus Count
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