How well can screening sensitivity and sojourn time be estimated

Chronic disease progression models are governed by two main parameters: preclinical intensity and sojourn time. The estimation of these parameters helps in optimizing screening programs (with an additional parameter: sensitivity of the screens), and we examine their effect in improving survival. Multiple approaches exist for estimating these parameters. However, these models are based on strong underlying assumptions. Our main aim is to investigate the effect of these assumptions. For this purpose, we developed a simulator to mimic a breast cancer screening program while directly observing the exact onset and the sojourn time of the disease. We then examine the performance of the model under different parameterizations and investigate the effects of different models on the sensitivity, the inter-screening intervals and misspecification of the used parametric distributions. Our results indicate a strong correlation among the estimated parameters. Besides, the underlying assumptions have a strong effect on the overall performance of the model. These findings shed a light on the seemingly discrepant results obtained by different authors using the same data sets but different assumptions.