Navigate difficult trade-off decisions with confidence
From early stage development through lifecycle management, we help you quantify and navigate the most difficult decisions. Our clients benefit from a global team of statisticians, modelers, epidemiologists, and analysts with expertise in advanced predictive modeling and simulation.
Our Certara team is proficient in developing and applying a variety of modelling techniques:
- Population-based modelling such as Markov models, discrete-event simulation, decision-analytic models
- Individual patient-level simulations such as microsimulation models, agent-based models
- Bayesian modelling
- Machine-learning models such as neural networks, deep learning
- Health economic modelling
What makes us unique?
- Thoughtful definition of the modeling framework and scope
- Transparent and collaborative model building process
- Advanced and broad modeling skill
Superior quantitative evidence synthesis
We are experts in a wide range of proven methods:
- Meta-analysis and model-based meta-analysis (MBMA)
- Advanced indirect treatment comparison (ITC) methods, combining patient-level and aggregate data, randomized control trials and real-world studies
- (Bayesian) network meta-analysis and network meta-regression (à link to case study)
- Matching adjusted indirect comparison (MAIC)
- Simulated treatment comparison (STC)
- Publication bias and heterogeneity assessment
- Between groups homogeneity testing
- Estimation of overpopulation dispersion
- Individual and trial-level surrogate endpoint validation
Clients also benefit from our CODEx Clinical Outcomes Databases, offering a ready-to-analyze platform for a large number of indications.
Advanced analytics to evaluate and predict benefit, risk and cost
Evaluate and predict the real-life benefit, risk and cost of new interventions and support internal decisions and evidence building for a product or application.
- Customized disease progression models. Explore potential clinical scenarios, predict a product’s value in an evolving landscape, and identify uncertainties and gaps in evidence
- Epidemiology forecasting models. Anticipate changes in therapy standards and populations for a disease and setting with models that consider the evolution of a disease and associated consequences
- Clinical program models. Evaluate probability of success for trials or programs under different scenarios and optimize choice of target population, sample size, and study endpoints.
Bridging to effectiveness studies
Real-world clinical studies require enormous time and resources. Yet clinical development data and many real-world data sources may still inform the expected or relative effectiveness.
We minimize the uncertainties between clinical trial efficacy and real world effectiveness so that you can succeed with outcomes predictions and payer engagements.
Bridging studies combine advanced predictive and integrative modeling with targeted clinical and real-life data analyses. They can be used to bridge from efficacy to effectiveness, from country to country, and from one population to another.
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Noemi Hummel Associate Director
Noemi Hummel Associate Director
Noemi holds a PhD in economics from the Swiss Federal Institute of Technology Zurich, and a master’s degree in applied mathematics from University of Heidelberg. She has more than 10 years of experience in working as a senior biostatistician and modelling projects direction. In Certara she has been involved in projects on statistical and economic modelling and applying evidence synthesis methods such as Bayesian network meta-analysis and model-based meta-analysis in a variety of disease areas including neuroscience, oncology, and rare diseases.
Noemi holds a PhD in economics from the Swiss Federal Institute of Technology Zurich, and a master’s degree in applied mathematics from University of Heidelberg. She has more than 10 years of experience in working as a senior biostatistician and modelling projects direction. In Certara she has been involved in projects on statistical and economic modelling and applying evidence synthesis methods such as Bayesian network meta-analysis and model-based meta-analysis in a variety of disease areas including neuroscience, oncology, and rare diseases.
