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Randomized controlled clinical trials provide the gold standard for evidencegeneration in relation to the effectiveness of a new treatment in medicalresearch. Relevant information from previous studies may be desirable toincorporate in the design of a new trial, with the Bayesian paradigm providinga coherent framework to formally incorporate prior knowledge. Many establishedmethods involve the use of a discounting factor, sometimes related to a measureof `similarity' between historical sources and the new trial. However, it isoften the case that the sample size is highly nonlinear in those discountingfactors. This hinders communication with subject-matter experts to elicitsensible values for borrowing strength at the trial design stage.  Focusing on a sample size formula that can incorporate historical data frommultiple sources, we propose a linearization technique such that the samplesize changes evenly over values of the discounting factors (hereafter referredto as `weights'). Our approach leads to interpretable weights that directlyrepresent the dissimilarity between historical and new trial data on theprobability scale, and could therefore facilitate easier elicitation of expertopinion on their values.  Inclusion of historical data in the design of clinical trials is not commonpractice. Part of the reason might be difficulty in interpretability ofdiscrepancy parameters. We hope our work will help to bridge this gap andencourage uptake of these innovative methods.  Keywords: Bayesian sample size determination; Commensurate priors; Historicalborrowing; Prior aggregation; Uniform shrinkage.

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