  Chronic disease progression models are governed by three main parameters:
sensitivity, preclinical intensity, and sojourn time. The estimation of these
parameters helps in optimizing screening programs and examine the improvement
in survival. Multiple approaches exist to estimate those parameters. However,
these models are based on strong underlying assumptions. The main aim of this
article is to investigate the effect of these assumptions. For this purpose, we
developed a simulator to mimic a breast cancer screening program directly
observing the exact onset and the sojourn time of the disease. We investigate
the effects of assuming the sensitivity to be constant, inter-screening
interval and misspecifying the sojourn time. Our results indicate a strong
correlation between the estimated parameters, and that the chosen sojourn
time-distribution has a strong effect on the accuracy of the estimation. These
findings shed a light on the seemingly discrepant results got by different
authors using the same data sets but different assumptions.
