Quantitative modeling plays an important role in medical decision-making, especially where high-quality data from randomized controlled trials is missing. A key challenge in developing such models is the integration of data and knowledge sources across different physical and temporal scales.
In this talk, we discuss how mathematical multi-scale models can be used to bridge the gap between biological mechanism and population level data. Based on research examples, we illustrate how these models can be used to evaluate the impact of biologic uncertainty at the decision level, and conversely, how population-level data can be used to improve our understanding of the underlying biologic mechanisms. Finally, we discuss how such models enable targeted prioritization of future research efforts through value of information analyses.
Multi-scale models constitute a valuable bridge between basic science research at the micro-scale and population level data and medical decision-making at the macro scale.