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, epidemiologists and analysts with expertise in advanced predictive modeling and simulation.

Our Certara team uses a variety of model types: Markov/cohort, decision analytic, Bayesian, patient-level simulation, discrete-event/Monte Carlo simulations, and typically include deterministic and probabilistic sensitivity analyses. What makes us unique:

  • Thoughtful definition of the modeling framework and scope
  • Transparent and collaborative model building process
  • Advanced and broad modeling skills
Superior quantitative evidence synthesis

We are experts in a wide range of proven methods:

  • Meta-analysis and network meta-analysis
  • Publication bias assessment
  • Heterogeneity assessment
  • Between groups homogeneity testing
  • Multivariate analyses
  • Estimation of overpopulation dispersion
  • Survival analysis, cross-over effect analysis
  • Linear models including prediction analysis
  • Bayesian hierarchical models
  • Individual and trial level surrogate validation
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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.  Optimize choice of target population, sample size, and study endpoints.

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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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Billy Amzal, PhD SVP, Decision and Real-World Data Analytics

With over 15 years of experience, Billy has developed more than a hundred advanced patient-level models and is an expert in study simulations, optimal design, Bayesian models and algorithms, quantitative decision analysis and real-world analytics.