The Decision Modeling Center is part of VU University Medical Center Amsterdam and allied to VU University Amsterdam. We conduct research in collaboration with clinicians and other researchers, both from the VU University Medical Center and outside of our institution. In addition, we do projects on behalf of pharmaceutical companies and the Dutch and European government.
Our team consists of about 20 experts who received training in a range of fields including mathematics, statistics, epidemiology, medical informatics, health sciences and biomedical sciences. We use our knowledge and creativity to gain insight into the complexity of medical decision problems thereby facilitating improved decision making.
Aim and scope
We aim to optimize decision making in public healthcare and in daily clinical practice by developing and using mathematical models.
Policy makers as well as clinicians are faced with many decision problems: new technologies that improve patient outcomes are developed continuously, but are often very expensive. Optimizing decisions is complex, due to the numerous decision options that are available and the uncertainty associated with subsequent benefits, harms and costs. We support decision making in healthcare by developing mathematical models that predict long-term consequences of possible decisions.
Expertise
We have expertise in a wide range of techniques for mathematical and statistical decision modeling such as:
- Cohort (Markov) modeling
- Patient-level micro-simulation modeling
- Prediction modeling for decision support
- Cost-effectiveness, cost-minimization, cost-benefit and budget-impact analyses
- Statistical modeling for classification problems
- Bayesian design and statistics
- Survival modeling
- Bayesian data synthesis
Areas of application
Our main areas of application are:
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- Public health. We make model-based predictions of the long-term effectiveness, cost-effectiveness and budget impact of prevention strategies for cervical cancer, colorectal cancer and diabetes.
- Healthcare policy planning. We build flexible disease models that allow for long-term prediction of health effects and costs of potentially new healthcare technologies, e.g. for optimizing lung cancer care.
- Clinical decision making. We develop decision tools that aid both doctor and patient to weigh the benefits and harms of several diagnostic and treatment options.