Bayesian sample size determination using robust commensurate priors with interpretable discrepancy weights

Randomized controlled clinical trials provide the gold standard for evidence generation in relation to the efficacy of a new treatment in clinical research. Relevant information from previous studies may be desirable to incorporate in the design and analysis of a new trial, with the Bayesian paradigm providing a coherent framework to formally incorporate prior knowledge. Many established methods involve the use of a discounting factor, sometimes related to a measure of 'similarity' between historical and the new trials. However, it is often the case that the sample size is highly nonlinear in those discounting factors. This hinders communication with subject-matter experts to elicit sensible values for borrowing strength at the trial design stage. Focussing on a method that can incorporate historical data from multiple sources, we highlight a particular issue of nonmonotonicity and explain why this causes issues with interpretability of discounting factors (hereafter referred to as 'weights'). We propose a solution from which an analytical sample size formula is derived. We then propose a linearization technique such that the sample size changes uniformly over the weights. This leads to interpretable weights (as a percentage of information to borrow/discount) which could facilitate easier elicitation of expert opinion on their values.