Bayesian evaluation of breast cancer screening using data from two studies

Abstract

The mean sojourn time (the duration of the period during which a cancer is symptom free but potentially detectable by screening) and the screening sensitivity (the probability that a screen applied to a cancer in the preclinical screen detectable period will result in a positive diagnosis) are two important features of a cancer screening programme. Little data from any single study are available on the potential effectiveness of mammographic screening for breast cancer in women with a family history of the disease, despite this being an important public health issue. We develop a method of estimation, from two separate studies, of the two parameters, assuming that transition from no disease to preclinical screen detectable disease, and from preclinical disease to clinical disease, are Poisson processes. Estimation is performed by a Markov chain Monte Carlo algorithm. The method is applied to the synthesis of two studies of mammographic screening in women with a family history of breast cancer, one in Manchester and one in Kopparberg, Sweden.