WHAT WE DO
Our Services
Custom modeling solutions for health economics & epidemiology, data analytics, and clinical trial simulation — tailored to the evidence challenges your project actually faces.
Health Economics
Our health economics practice combines deep methodological expertise with strategic insight to help life sciences organizations demonstrate value, support reimbursement, and make confident decisions across the product lifecycle. We build models, evidence packages, and analytical frameworks that withstand scrutiny from payers, HTA bodies, clinicians, and internal stakeholders.
We develop rigorous, decision‑grade economic models tailored to the clinical and commercial realities of your technology with clear documentation, transparent assumptions, and user‑friendly interfaces for internal and external stakeholders.
Model Types
- Cost‑effectiveness and cost‑utility models (bespoke or adaptations)
- State‑transition, partitioned survival, patient‑level microsimulation models
- Budget impact models and pricing models with risk-sharing schemes
Use Cases
- HTA‑ready economic models and country‑specific adaptations
- Early economic modeling to help identify value drivers and critical uncertainties to inform product development, pricing and stop-go decisions
- Model-based estimation of burden of illness
Epidemiology
Our practice possesses extensive expertise in epidemiological modeling, which differs from economic models by requiring careful attention to disease dynamics, incidence and prevalence, a specialized public health audience, unique data requirements, and methodological distinctions. Notably, epidemiological models estimate the impact of an intervention in the broader population including individuals who do not directly receive the intervention.
Model Types
- Dynamic transmission models
- Stochastic or deterministic compartmental models
- Agent-based models
Use Cases
- To analyze the temporal dynamics of disease transmission and evaluate the projected effects of interventions such as vaccination, isolation, or social distancing
- To assess the effectiveness and economic implications of specific public health measures, individually or in combination, at the population level
Data Analytics and Statistics
Health economic modeling often requires advanced statistical support because clinical trial SAPs may not produce the necessary outputs. We help you set a clear analytical strategy from the start, so your data, methods, and assumptions meet economic model and HTA needs.
- Statistical advice for analysis planning: recommending suitable methods based on your research goals, data structure, variable definitions, and model-ready outputs.
- Reanalyze clinical trial data with custom Statistical Analysis Plans for economic modeling.
- Perform subgroup analyzes to evaluate outcome variations by patient differences.
- Estimate trial-based resource use outcomes like hospitalizations, procedures, and medication use.
- Survival Analysis & Advanced Time to Event Modeling.
- Utility Mapping, Quality of Life Analysis & Regression Modeling - economic evaluations require preference based quality of life measures such as EQ 5D. When these are not collected directly, mapping provides a validated pathway to generate utilities from disease specific instruments.
- Regression based utility modeling to relate disease severity, biomarkers, or clinical outcomes to utility
Machine Learning
Machine learning involves techniques that provide more detailed insights from clinical, real-world, or simulated data. These techniques include creating predictive models to pinpoint value drivers and reveal patterns that aid strategic decision-making.
We build clear, reproducible classification, regression, and clustering models designed specifically for health-economic and evidence-generation purposes.
Clinical Trial Simulation (CTS)
Clinical trial simulations create synthetic trials that replicate real designs, including eligibility, randomisation, endpoints, schedules, and statistical methods. This produces datasets similar to actual trial data, letting sponsors test study performance across varying real-world conditions.
Pre‑trial:
Simulations assess the robustness of analyzes and objectives against issues like low adherence, confounding, insensitive measures, or diverse patients, and help optimise trial design by evaluating different sample sizes, endpoints, or strategies.
Post‑trial:
Simulations help interpret unexpected outcomes, evaluate protocol deviations, and explore scenarios not possible with observed data.
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