  The development of a new diagnostic test ideally follows a sequence of stages
which, amongst other aims, evaluate technical performance. This includes an
analytical validity study, a diagnostic accuracy study and an interventional
clinical utility study. Current approaches to the design and analysis of the
diagnostic accuracy study can suffer from prohibitively large sample sizes and
interval estimates with undesirable properties. In this paper, we propose a
novel Bayesian approach which takes advantage of information available from the
analytical validity stage. We utilise assurance to calculate the required
sample size based on the target width of a posterior probability interval and
can choose to use or disregard the data from the analytical validity study when
subsequently inferring measures of test accuracy. Sensitivity analyses are
performed to assess the robustness of the proposed sample size to the choice of
prior, and prior-data conflict is evaluated by comparing the data to the prior
predictive distributions. We illustrate the proposed approach using a
motivating real-life application involving a diagnostic test for ventilator
associated pneumonia. Finally, we compare the properties of the proposed
approach against commonly used alternatives. The results show that by making
better use of existing data from earlier studies, the assurance-based approach
can not only reduce the required sample size when compared to alternatives, but
can also produce more reliable sample sizes for diagnostic accuracy studies.
