Abstract
The rising costs of clinical trials for medical devices in recent years has led to an increased interest in so-called in silico clinical trials, where simulation results are used to supplement or to replace those obtained from human patients. Here we present a framework for executing such a trial. This framework relies heavily on ideas already developed for model verification, validation, and uncertainty quantification. The framework uses results from an initial cohort of human patients as model validation data, recognizing that the best model credibility evidence usually comes from real patients. The validation exercise leads to an assessment of the model’s suitability based on pre-defined acceptance criteria. If the model meets these criteria, then no additional human patients are required and the study endpoints that can be addressed using the model are met using the simulation results. Conversely, if the model is found to be inadequate, it is abandoned, and the clinical study continues using only human patients in a second cohort. Compared to other frameworks described in the literature based on Bayesian methods, this approach follows a strict model build-validate-predict structure. It can handle epistemic uncertainties in the model inputs, which is a common trait of models of biomedical systems. Another idea discussed here is that the outputs of engineering models rarely coincide with measures that are the basis for clinical endpoints. This manuscript discusses how the link between the model and clinical measure can be established during the trial.
Reliability and reproducibility in computational science: implementing validation, verification and uncertainty quantification
in silico