HEOR / HTARare Neuromuscular DiseaseCost-Effectiveness Modelling

Modernizing a Gene Therapy Cost-Effectiveness Model for HTA and Payer Decision-Making

Modernized a gene therapy cost-effectiveness model by integrating emerging clinical and real-world evidence, strengthening survival and utility assumptions, and implementing structured uncertainty analyses to improve the transparency, credibility, and decision usefulness of long-term value estimates.

Role: HEOR Consultant
Engine: Excel + VBA
Timeframe: ~1 year
Focus: Uncertainty & Value

Project Snapshot

Domain: HEOR · Health Technology Assessment · Rare disease gene therapy
Therapeutic area: Rare neuromuscular disease
Project type: Cost-effectiveness model enhancement & evidence integration
My role: HEOR consultant — model development, validation, uncertainty analysis, and stakeholder communication
Key analytical areas: PSA, DSA, scenario analysis, survival & utility modelling, extrapolation, discounting, validation
Stakeholders: HEOR/modelling team, project leadership, and client-facing HTA & payer stakeholders

Decision Context

How should long-term clinical benefits and quality-of-life improvements be translated into credible estimates of economic value for a gene therapy?

Decisions spanned how to represent long-term survival and disease progression, incorporate emerging clinical and real-world evidence, map clinical outcomes to health-state utilities, characterize uncertainty, and determine which assumptions belong in the base case versus scenario analyses — so outputs could support transparent, defensible HTA and payer discussions rather than a single point estimate.

Business Challenge

Short follow-up, decades-long projections

Gene therapies create substantial uncertainty for cost-effectiveness evaluation because relatively short clinical follow-up must often inform projections over decades.

The existing model needed to accommodate evolving evidence and alternative assumptions around survival, treatment effects, treatment initiation, utilities, costs, and time horizon — while clearly communicating their implications for cost-effectiveness.

A further challenge was ensuring uncertainty analysis was not simply added as a technical calculation, but integrated into the model architecture in a way that was transparent, reproducible, user-friendly, and suitable for HTA-oriented decision making.

Evidence vs. horizon

Clinical follow-up~years

Model time horizon~decades

The gap between observed data and projected value is where structural assumptions and uncertainty analysis do the heaviest lifting.

Solution

A flexible analytical platform, not a single estimate

From emerging evidence to HTA decision

01Clinical trial + RWE
02Survival / Effect / Utility
03CE Model
04Uncertainty Analysis
05HTA & Payer Decision

Structured uncertainty analysis

PSA, DSA, and scenario analysis to characterize parameter and structural uncertainty.

Flexible model architecture

Expanded age- and health-state calculations with user-controlled assumptions across inputs.

Evidence integration

Clinical trial, published, and real-world/natural-history evidence built into survival and utility.

Decision-oriented communication

Interfaces, summaries, visualizations, and documentation that expose the driving assumptions.

My Contribution

Translating evidence questions into implementable model solutions

01
Uncertainty frameworks
Designed and implemented PSA, DSA, and scenario analysis with parameter governance, distributions, and automated result generation.
02
VBA automation
Built VBA-based simulation and automation workflows to run repeated analyses and efficiently capture model outputs.
03
Survival & extrapolation
Evaluated alternative survival distributions and progression assumptions, and built selectable survival scenarios from competing evidence.
04
Utility mapping
Investigated translating clinical functional improvements into health-state utilities, including NSAA-related mapping and HTA precedents.
05
Validation & debugging
Identified and corrected implementation issues affecting age-specific costs, utilities, discounting, and model outputs.
06
Decision support & communication
Built interfaces, summary tables, visualizations, slides, and documentation to explain methods and recommendations to stakeholders.

Business Impact

The work strengthened the model as a decision-support and HTA evidence platform — not simply another set of cost-effectiveness estimates.

More credible estimates

Explicitly characterized parameter and structural uncertainty rather than relying on a single point estimate.

Testable assumptions

Let stakeholders see how survival, treatment effects, utilities, and horizon shift the value proposition.

Less manual work

VBA automation and reusable sensitivity workflows reduced repetitive analytical effort.

Higher confidence

Systematic validation surfaced and corrected implementation issues across the model.

The result is a more flexible architecture that can absorb future clinical or real-world data and support clearer communication of uncertainty and long-term value to payers and HTA bodies.

Methods & Technologies

HEOR & economic modelling

Cost-effectiveness analysis
Partitioned survival modelling
Long-term extrapolation
Discounted costs & QALYs

Uncertainty & sensitivity

PSA
DSA
Scenario analysis
Parameter distributions
CE plane & CEAC outputs

Survival & evidence

Survival distribution evaluation
Alternative survival sources
Treatment-effect assumptions
Data digitization
RWE integration

Health utilities

Health-state utility modelling
Outcome–utility mapping
NSAA-adjusted scenarios
HTA precedent review

Programming & implementation

Microsoft Excel
VBA
Automated simulation
Model interfaces & dashboards
Parameter governance
Data visualization

Deliverables

Enhanced Excel cost-effectiveness model
VBA-powered PSA simulation framework
Automated DSA and tornado-chart workflow
Scenario & survival-source analysis architecture
Model validation checks and corrected workflows
Stakeholder decks, interface, and documentation

Reusability

Several components form a reusable foundation for future HEOR models — reducing the effort to build more transparent and maintainable Excel-based economic models, and letting new evidence slot in without rewriting core calculations.

PSA framework
DSA framework
Scenario-analysis architecture
Parameter-governance template
Survival evidence comparison workflow
Model-validation workflow
User-interface principles

Project Gallery

Recreated, confidentiality-safe visuals — no product names, client identifiers, or proprietary values.

Fig. 01Anonymized

Uncertainty-analysis dashboard

Tornado diagram, cost-effectiveness plane, CEAC, and scenario comparison in one anonymized view.

Fig. 02Anonymized

Long-term survival scenario comparison

How alternative survival evidence and extrapolation assumptions propagate into long-term outcomes.

Fig. 03Anonymized

Model architecture & parameter governance

User-defined assumptions flow through calculations to outcomes, sensitivity analyses, and decision support.

Key Lessons

Build uncertainty into the architecture

Designing PSA, DSA, and scenario analysis alongside parameter governance makes a model easier to update, validate, and defend.

Structure drives long-term value

For gene therapies, survival extrapolation, effect duration, horizon, and utilities can move results as much as any single parameter.

Evidence needs an analytical bridge

Clinical or real-world signals must be justified against survival, progression, utilities, and costs before they become economic evidence.

The analyst-to-consultant transition happened at the recommendation layer — the highest-value work was not implementing assumptions, but judging which were defensible for the base case, which belonged in scenarios, and where uncertainty should stay explicit.