DAE

Abstract

Human papillomavirus (HPV) types 16 and 18 are associated with about 70% of cer- vical cancers. To evaluate the long-term benefits of cervical screening and vaccination against HPV, estimates of the natural history of HPV are required. A Markov model has previously been developed to estimate progression rates of HPV, through grades of neoplasia, to cancer. The model was fitted to cross-sectional data by age group from the UK, including data from a trial of HPV testing, population cervical screening data, and cancer registry data. Parameter uncertainties and model choices were originally only acknowledged by informal scenario analysis. We therefore reimplement this model in a Bayesian framework to take full account of parameter and model uncertainty. Assump- tions may then be weighted coherently according to how well they are supported by data. There is a complex network of evidence and parameters, involving misclassified and aggregated data, data available on dierent age groupings, and external data of indirect relevance. This is implemented as a Bayesian graphical model, and posterior distributions are estimated by MCMC. This work raises issues of uncertainty in complex evidence syntheses, and aims to encourage greater use in practice of techniques which are familiar in the statistical world.