Our Multidisciplinary Modeling Team
Our decision-analytic modeling team is composed of researchers with advanced degrees in industrial engineering, operations research, economics, health policy, and public health. Together, our teams bring a unique combination of skills that allow us to optimize complex processes and use advanced analytical methods to inform decision making, which is especially important for an industry as complex and nuanced as the pharmaceutical industry.
Decision-analytic models provide a framework for compiling clinical and economic evidence in a systematic fashion, determining your product’s value, and communicating that value to decision makers. The reliability of any model depends on the strength of the model structure and the quality of the data, but it must also be flexible, transparent, and user-friendly, which is why we staff all projects with senior researchers and modelers who have the technical training and design experience needed to ensure you receive the most intuitive and technically sound models possible.
Our multinational team has built hundreds of models, including:
- Cost-effectiveness models
- Budget impact models
- Cost-consequence analyses
- Asset valuation models
- Value-based pricing models
- Field tools, including cost calculators and interactive models
Whether you need a model to help understand unmet needs, guide strategic planning, conduct economic evaluations, or inform payer decisions, our robust design and validation methods will provide the level of credibility you need. And because your environment is dynamic and ever-changing, your models will be flexible and allow you to change inputs and assumptions to predict the impact of strategic decisions and changing market conditions.
As thought leaders in decision-analytic modeling, we offer deep experience in sophisticated modeling methods and can advise you on the best methodology given your objectives. Methods in which we have extensive training and experience include:
- Decision trees
- Markov and other stochastic process models
- Simulation (individual patient, discrete-event, etc.)
- Communicable disease models
- Linear, integer, and non-linear mathematical programming models
- Systematic and targeted reviews to identify and quality- appraise clinical, utility, and economic evidence
- Statistical analyses (survival analysis, multivariate regression, utility data analysis, etc.)
- Probabilistic, univariate, and structural sensitivity analysis
- Expected value of perfect information
- Multi-criteria decision analysis
From design to deliverables, publications, and ongoing support, we develop and validate models according to ISPOR, HTA, and other applicable guidelines. Our systematic approach includes:
- Gaining a thorough understanding of your needs and the value of your product
- Advising you on the type of model required
- Formulating the model structure, reviewing data and literature, and obtaining clinical validation when required
- Programming a user-friendly and transparent model
- Developing documents and publications to communicate the value of your product
- Providing training and consultation for utilizing the model
Throughout this process, we work closely with you to ensure the direction of research fully meets your needs. Our strong project management, validation, and quality control processes ensure we consistently provide you with the highest-quality deliverables on time and within budget.