Exploring the uncertainties of early detection results: model-based interpretation of mayo lung project
Autor: | Lu Shi, Shinyi Wu, William J. McCarthy, Haijun Tian, Rob J. de Boer, Barbara A. Berman |
---|---|
Přispěvatelé: | Public Health |
Jazyk: | angličtina |
Předmět: |
Male
Oncology medicine.medical_specialty Cancer Research Lung Neoplasms Early detection Adenocarcinoma of Lung Adenocarcinoma lcsh:RC254-282 Decision Support Techniques 03 medical and health sciences 0302 clinical medicine SDG 3 - Good Health and Well-being Internal medicine medicine Genetics Humans Mass Screening 030212 general & internal medicine Lung cancer Trial Arms Early Detection of Cancer Aged Aged 80 and over Lung business.industry Incidence Incidence (epidemiology) Uncertainty Middle Aged lcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens medicine.disease Hospitals 3. Good health Surgery Clinical trial medicine.anatomical_structure Cancer incidence Data Interpretation Statistical 030220 oncology & carcinogenesis business Lung cancer screening Research Article |
Zdroj: | BMC Cancer BMC Cancer, Vol 11, Iss 1, p 92 (2011) BMC Cancer, 11. BioMed Central Ltd. |
ISSN: | 1471-2407 |
DOI: | 10.1186/1471-2407-11-92 |
Popis: | Background The Mayo Lung Project (MLP), a randomized controlled clinical trial of lung cancer screening conducted between 1971 and 1986 among male smokers aged 45 or above, demonstrated an increase in lung cancer survival since the time of diagnosis, but no reduction in lung cancer mortality. Whether this result necessarily indicates a lack of mortality benefit for screening remains controversial. A number of hypotheses have been proposed to explain the observed outcome, including over-diagnosis, screening sensitivity, and population heterogeneity (initial difference in lung cancer risks between the two trial arms). This study is intended to provide model-based testing for some of these important arguments. Method Using a micro-simulation model, the MISCAN-lung model, we explore the possible influence of screening sensitivity, systematic error, over-diagnosis and population heterogeneity. Results Calibrating screening sensitivity, systematic error, or over-diagnosis does not noticeably improve the fit of the model, whereas calibrating population heterogeneity helps the model predict lung cancer incidence better. Conclusions Our conclusion is that the hypothesized imperfection in screening sensitivity, systematic error, and over-diagnosis do not in themselves explain the observed trial results. Model fit improvement achieved by accounting for population heterogeneity suggests a higher risk of cancer incidence in the intervention group as compared with the control group. |
Databáze: | OpenAIRE |
Externí odkaz: |