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. Interpreting the available information and optimizing decisions is difficult, especially if there are many uncertainties.
We support decision making in healthcare by developing mathematical models that predict long-term consequences of possible decisions. We balance benefits of new technologies against the additional costs in order to use limited healthcare resources in a sensible manner.
Our strength is our statistical and mathematical expertise. We assess and synthesize the available data, thereby dealing with heterogeneous data sources, and carry out methodological research on the edge between mathematical statistics and practical clinical problems.
We aid public health decisions by making model-based predictions of the long-term effectiveness, cost-effectiveness and budget impact of prevention strategies. In addition, we carry out analyses on (pooled) data from registries, randomized controlled trials and cohort studies to inform decision models.
Cervical cancer prevention
Colorectal cancer prevention
We contribute to healthcare policy planning by analyzing the impact of different strategies for diagnosis, treatment and surveillance. We do this by building flexible disease models that allow for long-term prediction of health effects and costs of potentially new healthcare technologies.
Optimizing lung cancer care
Integrated modeling of cancer progression and care
Clinical Decision Making
To assist decision making in daily clinical practice, we support the development of decision tools. These tools aid both doctor and patient to weigh the benefits and harms of several diagnostic and treatment options, while taking patient preferences for quality and quantity of life into account.
We have experience 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
- and more
We conduct research in collaboration with clinicians and other researchers, both from the VU medical Center as well as outside of our institution. In addition, we do projects on behalf of pharmaceutical companies and the Dutch and European government.
Our team consists of experts who received training in a range of fields, including mathematics, statistics, epidemiology, medical informatics, health sciences and biomedical sciences. These experts use their knowledge and creativity to gain insight into the complexity of medical decision problems thereby facilitating improved decision-making.