How does early detection by screening affect disease progression? Modeling estimated benefits in prostate cancer screening

Abstract

Background: Simulation models are essential tools for estimating benefits of cancer screening programs. Such models include a screening-effect model that represents how early detection by screening followed by treatment affects disease-specific survival. Two commonly used screening-effect models are the stage-shift model, where mortality benefits are explained by the shift to more favorable stages, and the cure model, where early detection enhances the chances of cure from disease.

Objective: This article describes commonly used screening-effect models and analyses their predicted mortality benefit in a model for prostate cancer screening.

Method: The MISCAN simulation model was used to predict the reduction of prostate cancer mortality in the European Randomized Study of Screening for Prostate Cancer (ERSPC) Rotterdam. The screening-effect models were included in the model. For each model the predictions of prostate cancer mortality reduction were calculated. The study compared 4 screening-effect models, which are versions of the stage-shift model or the cure model.

Results: The stage-shift models predicted, after a follow-up of 9 years, reductions in prostate cancer mortality varying from 38% to 63% for ERSPC-Rotterdam compared with a 27% reduction observed in the ERSPC. The cure models predicted reductions in prostate cancer mortality varying from 21% to 27%.

Conclusions: The differences in predicted mortality reductions show the importance of validating models to observed trial mortality data. The stage-shift models considerably overestimated the mortality reduction. Therefore, the stage-shift models should be used with care, especially when modeling the effect of screening for cancers with long lead times, such as prostate cancer.

Figure 1

Modeling the impact of early detection on disease progression. Each panel shows relevant events in an individual life history on a time line. Panel A indicates the time of death from other causes than prostate cancer (Death OC). Panel B presents the prostate cancer history in the absence screening. Survival S after clinical diagnosis (Clin Dx) equals a random variable X, drawn from a survival curve specific for stage at diagnosis. Panels C to E illustrate the impact of early detection by screening in the various models. Panel C: With the Stage-shift 1 model, survival S' after detection by screening (Scr Dx) equals the sum of lead-time L and a random variable X' drawn from a survival curve specific for the stage at the time of early detection. Panel D: With the Stage-shift 2 model X' is generated similarly, but survival after detection by screening S' is taken to be the maximum of the original survival (L+X) and the new X'. Panel E: In the cure models, survival S' is up to death from other cause (Death OC) with probability c (cure) and equals the original survival corrected for lead-time (L+X) with probability 1-c (no cure).

Figure 2

Predicted prostate cancer mortality reduction by follow-up time for men aged 55-69 at the first screening, using the different sub-models for the effect of screening. The dot shows the observed 27% mortality reduction at nine years of follow-up in the ERSPC. Stage-shift model 1: the cancer-specific survival starts after the lead-time. Stage-shift model 2: the survival starts at screen-detection but is taken as the maximum of the new and the original survival, Cure model 1: assuming a constant cure rate. Cure model 2: assuming a cure rate that depends on the lead-time.