How Evidence Synthesis Supports Economic Models

In an era of increasingly rigorous scrutiny by health technology assessment (HTA) bodies, the bar for economic modeling has never been higher. Sophisticated cost-effectiveness models and budget impact analyses are critical tools for demonstrating the value of new interventions — but their integrity and credibility hinge on one essential input: evidence synthesis in economic modeling.

What many forget is that evidence isn’t just a static dataset pulled from a trial report or clinical study. Instead, it requires methodologically sound, transparent, and purpose-built evidence synthesis — typically in the form of systematic, scoping, or targeted literature reviews. This synthesis forms the foundation on which models are built, assumptions are justified, and value is defended.

This article explores how evidence synthesis supports economic modeling, why it is often underutilized, and how to approach it strategically for HTA success.

The Disconnect: Models First, Evidence Later?

All too often, modeling and evidence synthesis teams operate in silos. The economic model is developed by one team (often outsourced to modeling vendors), and literature review is treated as a downstream documentation task. This retrospective approach leads to serious gaps:

• Model assumptions that lack justification
• Missing or outdated data inputs
• Rejection of economic models by HTA agencies
• Delays in market access and reimbursement

Instead, evidence synthesis must be a foundational, integrated component of economic modeling — not a post hoc fix.

What Is Evidence Synthesis in This Context?

Evidence synthesis refers to the structured, transparent, and reproducible process of identifying, selecting, and analyzing data from published (and sometimes unpublished) literature. Depending on the objective, this may involve:

• Systematic Literature Reviews (SLRs) for comprehensiveness and rigor
• Targeted Literature Reviews (TLRs) for speed and specificity
• Scoping Reviews to map and assess the breadth of evidence
• Rapid Reviews for time-sensitive payer needs

Each of these methods has its place in the development of health economic models, and their value is often underestimated.

Bridging the Gap: 5 Ways Evidence Synthesis Powers Economic Models

Here are five core areas where literature review directly supports and enhances the quality, credibility, and relevance of cost-effectiveness models:

1. Defining the Model Structure with Precision

Before any equations are written or spreadsheets built, the model must reflect:

• The correct population (e.g., inclusion/exclusion criteria from real-world settings)
• The right comparators (standard of care, not just placebo or outdated regimens)
• Relevant clinical and economic outcomes (survival, QALYs, costs, etc.)

Evidence synthesis helps:

• Align these elements with HTA expectations
• Provide real-world context on treatment pathways and clinical practice patterns
• Inform the model framework (e.g., decision tree, Markov model, partitioned survival)

Without this foundation, the model can be misaligned with payer needs from the outset.

2. Populating Key Model Inputs with High-Quality Data

Economic models require an extensive range of inputs. These include:

• Clinical Effectiveness: Transition probabilities, hazard ratios, median survival
• Safety Outcomes: Adverse event incidence, discontinuation rates
• Resource Utilization: Hospitalization, outpatient visits, diagnostics
• Costs: Drug acquisition, administration, follow-up
• Utility Values: Health-state specific HRQoL scores

A targeted or systematic review provides these data from peer-reviewed sources, justifying every assumption. This is especially crucial when:

• RCT data is incomplete or doesn’t match the model scope
• Subgroup data is needed (e.g., elderly, comorbid populations)
• HTA bodies require external validation of submitted model inputs

3. Supporting Assumptions and Justifying Uncertainty

No model is free from assumptions. In fact, assumptions are expected — but they must be:

• Clearly stated
• Justified with data
• Tested through sensitivity and scenario analyses

Evidence synthesis plays a vital role in:

• Identifying a range of plausible values from multiple studies
• Highlighting variation in real-world outcomes
• Providing contextual evidence to explain modeling choices

This supports probabilistic sensitivity analysis (PSA) and helps defend the model under scrutiny.

4. Enhancing Transparency and Methodological Credibility

HTA agencies are becoming increasingly meticulous in their reviews of modeling methodology. Many now demand full documentation of how each model input was sourced. A few trends:

• NICE (UK) expects justification for every clinical input and encourages submission of systematic review protocols
• CADTH (Canada) may reject cost inputs not backed by real-world literature
• PBAC (Australia) often requests detailed appendices with literature search strategies

Robust evidence synthesis, especially when conducted using PRISMA guidelines and HTA-aligned protocols, ensures that:

• Each input is traceable
• Every data point is defensible
• The model is auditable

This level of transparency can prevent delays, data queries, and even rejection.

5. Identifying Evidence Gaps and Informing Model Development

Sometimes, what evidence synthesis doesn’t find is just as valuable as what it does.

By mapping the existing literature, you can:

• Spot evidence gaps that need to be addressed through primary research
• Avoid overfitting models to limited or unrepresentative data
• Make proactive adjustments to modeling strategies based on data availability

This insight can guide internal teams on what data to generate (e.g., PROs, RWE) and shape future study design or label development.

A Real-World Example

A biotech firm preparing a CEA for a rare oncology indication came to us with a partially built model. Key inputs — including utility values, treatment durations, and adverse event costs — were sourced from non-specific literature and expert opinion.

We rebuilt the evidence base by conducting:

• A targeted literature review focused on utilities and real-world costs
• A mini systematic review of comparator effectiveness across smaller phase II/III trials
• An annotated evidence dossier aligning each input with its source and rationale

The result?

• The model was accepted by the HTA agency with no requests for evidence clarification
• A reimbursement decision was secured 3 months faster than projected
• The evidence package was reused across 3 additional EU markets

This shows how fit-for-purpose evidence synthesis can directly impact time-to-access and payer acceptance.

Best Practices for Integrating Evidence Synthesis and Economic Modeling

To unlock the full value of literature reviews in economic modeling, consider these best practices:

1. Start Early – Don’t wait until the model is built to search for data. Literature review should begin during model conceptualization.
2. Define Your Questions Clearly – Use the PICO framework to frame your review scope and make it HTA-aligned.
3. Choose the Right Review Type – Don’t overuse systematic reviews where a TLR will suffice; don’t rely on targeted reviews where transparency is required.
4. Document Everything – Search strategies, screening logs, appraisal criteria, and decisions should all be retained.
5. Embed Cross-Functional Collaboration – Ensure evidence synthesis experts, modelers, HEOR leads, and access teams work in tandem.

Final Thoughts

In a landscape where every assumption is questioned, every data source challenged, and every decision scrutinized — the role of evidence synthesis in economic modeling is foundational.

It doesn’t just bridge data and models. It builds the trust necessary for payers and HTA bodies to accept those models as credible, relevant, and ultimately, persuasive.

As the stakes for reimbursement grow higher, companies that treat evidence synthesis as a strategic asset — not a formality — will be better positioned to win access, accelerate launches, and prove value.

📌 Need support building evidence-driven models or developing HTA-ready literature reviews?
At Vedara-IQ (an i-Qode company), we specialize in integrating evidence synthesis with advanced economic modeling to deliver payer-ready, defensible value communication. Our HEOR and medical writing experts ensure every model input is transparent, credible, and HTA-aligned — helping you accelerate reimbursement decisions and secure market access with confidence.

👉 Reach out at info@i-Qode.com to learn how we can support your next HTA submission.