Health Economics & Outcomes Modeling

Reimbursement conversations eventually arrive at one question: is the added benefit worth the added cost? Cost-effectiveness and cost-utility analyses answer it in the units HTA bodies work with, cost per QALY and per life-year gained, and reviewers will take the model apart line by line before they accept the answer.

MadeAi builds CEA and CUA models designed for exactly that scrutiny: transparent structure, documented assumptions, and every efficacy, utility, and cost input traceable to a systematically reviewed source. Full sensitivity and scenario analyses show decision-makers not just the result, but how much they can trust it.

Cost-effectiveness analysis is commonly used for:

• HTA submissions and reimbursement dossiers
• Pricing and value-based negotiation support
• Cost per QALY and life-year gained estimates
• Uncertainty and scenario analysis for decision confidence

Before a payer asks whether a therapy is cost-effective, they ask a more immediate question: what happens to our budget if we cover it? A credible budget impact model answers in the payer’s own terms, their population, their horizon, their cost structure.

MadeAi delivers budget impact models that are transparent enough to defend and flexible enough to localize: uptake and market-mix scenarios payers recognize, inputs drawn from a traceable evidence base, and a structure affiliates can adapt to each market without breaking the logic. The conversation starts with credibility instead of caveats.

Budget impact models are commonly used for:

• Formulary and coverage decision support
• Affordability planning across payer populations
• Country and plan-level model adaptations
• Uptake and market-share scenario testing

Chronic and progressive diseases don’t resolve in a single step, and neither should the models that represent them. Markov transition-state models and patient-level simulations capture how patients move through health states over time, so long-term costs and outcomes reflect the true course of disease rather than a snapshot.

MadeAi designs and builds these models with clinical and health economics experts working together: health-state structures grounded in the natural history literature, transition probabilities sourced from systematic review, and patient-level simulation where population heterogeneity or treatment history genuinely changes the answer. Structure first, spreadsheet second.

Markov and patient-level models are commonly used for:

• Modeling chronic and progressive disease pathways
• Long-term cost and outcome extrapolation
• Survival modeling beyond trial follow-up
• Capturing patient heterogeneity in economic results

The comparator that matters to a payer is rarely the one in your trial. When head-to-head data doesn’t exist, network meta-analysis and indirect treatment comparisons provide the comparative effect estimates that HTA submissions and economic models depend on, and assessors expect the methodology to hold up.

MadeAi grounds every NMA in a systematically identified trial network, assesses feasibility before committing, and documents heterogeneity, model choice, and fit to the standards HTA reviewers apply. The comparative estimates flow straight into your CEA and value dossier, one connected evidence chain from search to submission.

Network meta-analysis is commonly used for:

• Comparative effectiveness without head-to-head trials
• Effect estimates for economic model inputs
• HTA and JCA comparative evidence requirements
• Feasibility and network connectivity assessment