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Open Access Highly Accessed Research article

Exploring the uncertainties of early detection results: model-based interpretation of mayo lung project

Lu Shi15*, Haijun Tian2, William J McCarthy35, Barbara Berman3, Shinyi Wu4 and Rob Boer5

Author Affiliations

1 Department of Health Services, 650 Charles E. Young Drive S. 61-253 CHS, Los Angeles, CA 90095, USA

2 Health Benchmarks, Inc, IMS Health, 21650 Oxnard St, Ste 550, Woodland Hills, CA, USA

3 UCLA Division of Cancer Prevention and Control Research, Los Angeles, CA, USA

4 Epstein Department of Industrial and Systems Engineering, University of Southern California, Los Angeles, CA, USA

5 Erasmus MC, University Medical Center Rotterdam, The Netherlands

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BMC Cancer 2011, 11:92  doi:10.1186/1471-2407-11-92

Published: 7 March 2011

Abstract

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.