Use of the Metropolis-Hastings algorithm in the calibration of a patient level simulation of prostate cancer screening

Introduction • Designing cancer screening programmes requires an understanding of epidemiology, disease natural history and screening test characteristics. • Many of these aspects of the decision problem are unobservable and data can only tell us about their joint uncertainty. • A Metropolis-Hastings algorithm was used to calibrate a patient level simulation model of the natural history of prostate cancer to national cancer registry and international trial data. • This method correctly represents the joint uncertainty amongst the model parameters by drawing efficiently from a high dimensional correlated parameter space. • The calibration approach estimates the probability of developing prostate cancer, the rate of disease progression and sensitivity of the screening test. • This is then used to estimate the impact of prostate cancer screening in the UK. • This case study demonstrates that the Bayesian approach to calibration can be used to appropriately characterise the uncertainty alongside computationally expensive simulation models.