Quantitative information can be measured, inferred, or predicted, and the process used to assess the credibility of such information varies depending on how it is produced. In this paper, we propose a general process called the 7S Framework, which can be used to assess the credibility of information, whether measured, inferred or predicted. The 7S Framework integrates and generalises the credibility assessment approaches used for measured information in metrology, inferred information in statistics, and predicted information in computational science and engineering. In this study, when applied to seven fairly different use cases, the proposed framework was effective, sufficiently general, and capable of capturing all the subtle differences that the concept of credibility implies for these different kinds of information. We propose the 7S Framework as a generalisation useful in the credibility assessments of complex in silico medicine scenarios such as in-silico augmented clinical trials, physics-informed machine learning predictors, or the use of synthetic datasets to overcome privacy limitations, train machine learning predictors, and run large-scale In Silico Trials.

This is the preprint of the paper entitled "Assessing the credibility of quantitative information: a general framework" by Marco Viceconti, to be submitted to NPJ Digital Medicine for consideration for publication.