Bayesian evaluation of breast cancer screening using data from two studies.
Authors
Myles, Jonathan PNixon, 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.ukIssue Date
2003-05-30
Metadata
Show full item recordAbstract
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 MedJournal
Statistics in MedicineDOI
10.1002/sim.1365PubMed ID
12720303Type
ArticleLanguage
enISSN
0277-6715ae974a485f413a2113503eed53cd6c53
10.1002/sim.1365