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Reducing the effects of lead-time bias, length bias and over-detection in evaluating screening mammography: a censored bivariate data approach

Department of Biostatistics, Center for Biostatistics and Advanced Informatics, University of Kansas Medical Center, MSN 1026, 3901 Rainbow Blvd., Kansas City, KS 66160, USA, jmahnken{at}kumc.edu

Wenyaw Chan

Division of Biostatistics, School of Public Health, University of Texas - Health Science Center, 1200 Herman Pressler, Houston, TX 77030, USA

Daniel H Freeman, Jr

Office of Biostatistics, Department of Preventive Medicine and Community Health, University of Texas Medical Branch, 700 Harborside Drive, Galveston, TX 77555-1148, USA

Jean L Freeman

Sealy Center on Aging, Department of Internal Medicine, Department of Preventive Medicine and Community Health, University of Texas Medical Branch, 301 University Blvd., Galveston, TX 77555-0460, USA

Measuring the benefit of screening mammography is difficult due to lead-time bias, length bias and over-detection. We evaluated the benefit of screening mammography in reducing breast cancer mortality using observational data from the SEER-Medicare linked database. The conceptual model divided the disease duration into two phases: preclinical (T₀) and symptomatic (T₁) breast cancer. Censored information for the bivariate response vector ( T₀, T₁) was observed and used to generate a likelihood function. However, the contribution to the likelihood function for some observations could not be calculated analytically, thus, censoring boundaries for these observations were modified. Inferences about the impact of screening mammography on breast cancer mortality were made based on maximum likelihood estimates derived from this likelihood function. Hazard ratios (95% confidence intervals) of 0.54 (0.48—0.61) and 0.33 (0.26— 0.42) for single and regular users (vs. non-users), respectively, demonstrated a protective effect of screening mammography among women 69 years and older. This method reduced the impact of lead-time bias, length bias and over-detection, which biased the estimated hazard ratios derived from standard survival models in favour of screening.

This version was published on December 1, 2008

Statistical Methods in Medical Research, Vol. 17, No. 6, 643-663 (2008)
DOI: 10.1177/0962280207087309


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