A Bayesian approach to sharing information on sensitivity of a Multi-Cancer Early Detection test across and within tumour types and stages

The Galleri (R) (GRAIL) multi-cancer early detection test measures circulating tumour DNA (ctDNA) to predict the presence of more than 50 different cancers, from a blood test. If sensitivity of the test to detect early-stage cancers is high, using it as part of a screening programme may lead to better cancer outcomes, but available evidence indicates there is heterogeneity in sensitivity between cancer types and stages. We describe a framework for sharing evidence on test sensitivity between cancer types and/or stages, examining whether models with different sharing assumptions are supported by the evidence and considering how further data could be used to strengthen inference. Bayesian hierarchical models were fitted, and the impact of information sharing in increasing precision of the estimates of test sensitivity for different cancer types and stages was examined. Assumptions on sharing were informed by evidence from a review of the literature on the determinants of ctDNA shedding and its detection in a blood test. Support was strongest for the assumption that sensitivity can be shared only across stage 4 for all cancer types. There was also support for the assumption that sensitivities can be shared across cancer types for each stage, if cancer types expected to have low sensitivity are excluded which increased precision of early-stage cancer sensitivity estimates and was considered the most appropriate model. High heterogeneity limited improvements in precision. For future research, elicitation of expert opinion could inform more realistic sharing assumptions.