Modeling the mean time to interval cancer after negative results of periodic cancer screening.

Abstract

Interval cancers are cancers detected symptomatically between screens or after the last screen. A mathematical model for the development of interval cancers can provide useful information for evaluating cancer screening. In this regard a useful quantity is MIC, the mean duration in years of progressive preclinical cancer (PPC) that leads to interval cancers. Estimation of MIC involved extending a previous model to include three negative screens, invoking the multinomial-Poisson transformation to avoid estimating background cancer trends, and varying screening test sensitivity. Simulations show that when the true MIC is 0.5, the method yields a reasonably narrow range of estimated MICs over the range of screening test sensitivities from 0.5 to 1.0. If the lower bound on the screening test sensitivity is 0.7, the method performs considerably better even for larger MICs. The application of the method involved annual lung cancer screening in the Prostate, Lung, Colorectal, and Ovarian trial. Assuming a normal distribution for PPC duration, the estimated MIC (95% confidence interval) ranged from 0.00 (0.00 to 0.34) at a screening test sensitivity of 1.0 to 0.54 (0.03, 1.00) at a screening test sensitivity of 0.5 Assuming an exponential distribution for PPC duration, which did not fit as well, the estimated MIC ranged from 0.27 (0.08, 0.49) at a screening test sensitivity of 0.5 to 0.73 (0.32, 1.26) at a screen test sensitivity of 1.0 Based on these results, investigators may wish to investigate more frequent lung cancer screening.