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

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 (T(0)) and symptomatic (T(1)) breast cancer. Censored information for the bivariate response vector ( T(0), T(1)) 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.