Design of Experiments for Model Discrimination Hybridising Analytical and Data-Driven Approaches
This addresses a specific bottleneck for healthcare companies needing interpretable model discrimination for regulatory approval, representing an incremental improvement over prior analytical or data-driven methods.
The paper tackles the problem of discriminating between competing computational models in healthcare regulatory submissions when data is scarce, by developing a hybrid approach using Gaussian process surrogates that extends existing methods to non-analytical models with computational efficiency.
Healthcare companies must submit pharmaceutical drugs or medical devices to regulatory bodies before marketing new technology. Regulatory bodies frequently require transparent and interpretable computational modelling to justify a new healthcare technology, but researchers may have several competing models for a biological system and too little data to discriminate between the models. In design of experiments for model discrimination, the goal is to design maximally informative physical experiments in order to discriminate between rival predictive models. Prior work has focused either on analytical approaches, which cannot manage all functions, or on data-driven approaches, which may have computational difficulties or lack interpretable marginal predictive distributions. We develop a methodology introducing Gaussian process surrogates in lieu of the original mechanistic models. We thereby extend existing design and model discrimination methods developed for analytical models to cases of non-analytical models in a computationally efficient manner.