In this section
We strive to find solutions to new problems, and develop methods that can reduce the complexity of operational and policy decisions. We are aware of, and whenever possible try to avoid, the shortfalls associated with the often simplistic, albeit widely accepted, assumptions of several standard modeling and statistical approaches.
In particular, we constantly look for new ways to extract information from existing data, and to correctly assess the dynamical impact of self-interactions within and cross-interactions between health, socio-economic determinants, and environmental determinants, and to take into account any factor that, if neglected, may lower the quality of model results, and increase risk when models are used to inform real-world decisions.
Areas that we are currently investigating include:
- The use of micro-simulation of market behavior in health economic evaluations;
- Multi-scale phenomena in epidemiology and in healthcare markets;
- Methods to identify and quantify non-linear correlations;
- Inference of survival curves.