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American Journal of Epidemiology Advance Access originally published online on May 25, 2008
American Journal of Epidemiology 2008 168(1):98-104; doi:10.1093/aje/kwn120
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American Journal of Epidemiology © The Author 2008. Published by the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org.

PRACTICE OF EPIDEMIOLOGY

Correcting for Lead Time and Length Bias in Estimating the Effect of Screen Detection on Cancer Survival

Stephen W. Duffy1, Iris D. Nagtegaal2, Matthew Wallis3, Fay H. Cafferty1, Nehmat Houssami4, Jane Warwick1, Prue C. Allgood1, Olive Kearins5, Nancy Tappenden5, Emma O'Sullivan5 and Gill Lawrence5

1 Cancer Research UK Centre for Epidemiology, Mathematics and Statistics, Wolfson Institute for Preventive Medicine, London, United Kingdom
2 Department of Pathology, Dutch Cancer Society, Nijmegen, the Netherlands
3 Cambridge Breast Unit, Addenbrooke's Hospital, Cambridge, United Kingdom
4 Screening and Test Evaluation Program, School of Public Health, University of Sydney, Sydney, Australia
5 West Midlands Breast Screening Quality Assurance Reference Centre, University of Birmingham, Birmingham, United Kingdom

Correspondence to Dr. Prue Allgood, Cancer Research UK Centre for Epidemiology, Mathematics and Statistics, Wolfson Institute for Preventive Medicine, Charterhouse Square, London WC1M 6BQ, United Kingdom (e-mail: prue.allgood{at}cancer.org.uk).

Received for publication December 21, 2007. Accepted for publication April 8, 2008.

Determination of survival time among persons with screen-detected cancer is subject to lead time and length biases. The authors propose a simple correction for lead time, assuming an exponential distribution of the preclinical screen-detectable period. Assuming two latent categories of tumors, one of which is more prone to screen detection and correspondingly less prone to death from the cancer in question, the authors have developed a strategy of sensitivity analysis for various magnitudes of length bias. Here they demonstrate these methods using a series of 25,962 breast cancer cases (1988–2004) from the West Midlands, United Kingdom.

bias (epidemiology); breast neoplasms; mass screening; models, statistical; survival

Abbreviations: CI, confidence interval


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