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

2.50
Hdl Handle:
http://hdl.handle.net/10541/78242
Title:
Bayesian evaluation of breast cancer screening using data from two studies.
Authors:
Myles, Jonathan P; Nixon, Richard M; Duffy, Stephen W; Tabar, Laszlo; Boggis, C; Evans, D Gareth R; Shenton, Andrew; Howell, Anthony ( 0000-0002-3879-5991 )
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.
Affiliation:
Department of Mathematics, Statistics and Epidemiology, Cancer Research UK, London, UK. jonathan.myles@cancer.org.uk
Citation:
Bayesian evaluation of breast cancer screening using data from two studies. 2003, 22 (10):1661-74 Stat Med
Journal:
Statistics in Medicine
Issue Date:
30-May-2003
URI:
http://hdl.handle.net/10541/78242
DOI:
10.1002/sim.1365
PubMed ID:
12720303
Type:
Article
Language:
en
ISSN:
0277-6715
Appears in Collections:
All Christie Publications

Full metadata record

DC FieldValue Language
dc.contributor.authorMyles, Jonathan P-
dc.contributor.authorNixon, Richard M-
dc.contributor.authorDuffy, Stephen W-
dc.contributor.authorTabar, Laszlo-
dc.contributor.authorBoggis, C-
dc.contributor.authorEvans, D Gareth R-
dc.contributor.authorShenton, Andrew-
dc.contributor.authorHowell, Anthony-
dc.date.accessioned2009-08-21T14:38:04Z-
dc.date.available2009-08-21T14:38:04Z-
dc.date.issued2003-05-30-
dc.identifier.citationBayesian evaluation of breast cancer screening using data from two studies. 2003, 22 (10):1661-74 Stat Meden
dc.identifier.issn0277-6715-
dc.identifier.pmid12720303-
dc.identifier.doi10.1002/sim.1365-
dc.identifier.urihttp://hdl.handle.net/10541/78242-
dc.description.abstractThe 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.en
dc.language.isoenen
dc.subjectBreast Canceren
dc.subject.meshAdult-
dc.subject.meshAlgorithms-
dc.subject.meshBayes Theorem-
dc.subject.meshBreast Neoplasms-
dc.subject.meshEngland-
dc.subject.meshFemale-
dc.subject.meshHumans-
dc.subject.meshIncidence-
dc.subject.meshLikelihood Functions-
dc.subject.meshMammography-
dc.subject.meshMarkov Chains-
dc.subject.meshMass Screening-
dc.subject.meshMiddle Aged-
dc.subject.meshMonte Carlo Method-
dc.subject.meshPrevalence-
dc.subject.meshSensitivity and Specificity-
dc.subject.meshSweden-
dc.titleBayesian evaluation of breast cancer screening using data from two studies.en
dc.typeArticleen
dc.contributor.departmentDepartment of Mathematics, Statistics and Epidemiology, Cancer Research UK, London, UK. jonathan.myles@cancer.org.uken
dc.identifier.journalStatistics in Medicineen
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