Validating MEA Recommendations with Real-World Precedents

After identifying potential MEAs, you need to ensure they are practical and have been successfully implemented before. By the end of this lesson you'll be able to explain how the case study layer validates MEA recommendations using real-world precedent data.

Core idea

The case study layer is an optional module that validates recommended MEAs by searching a large library of existing agreements for real-world precedents. This layer helps you understand if a proposed MEA has been used successfully in similar contexts, providing crucial data to support negotiations. It works by filtering a vast database of past agreements based on criteria like market, therapeutic area, and the MEA type itself. This process helps you gauge the precedent confidence for each recommended MEA, indicating how commonly and successfully it has been applied.

The case study layer is triggered on demand, allowing you to focus on precedent validation only when needed. It requires a few additional inputs, such as priority markets (e.g., US, EU4) and the therapeutic area of the asset. These inputs refine the search, ensuring the precedents found are highly relevant to your specific situation. The system then cross-checks the filtered library against each recommended MEA, providing details like the number of matching agreements, the markets where they occurred, and their outcomes.

Walkthrough

Let's walk through how the case study layer validates an MEA recommendation.

Task: Trigger the case study layer and interpret the results.

1. Initiate the search: After the MEA selection tool provides its ranked recommendations, you click a "Find precedents" button.

2. Provide additional inputs:

   • You select "EU4" and "UK" as your priority markets.
   • You select "Oncology" as the therapeutic area.

3. System filters the library: The tool automatically filters its library of 8,000+ agreements using these inputs, along with the recommended MEA types and the last 10 years of data. Agreements from the last 5 years are given a recency weight (they count double).

4. Cross-check and compute results: For each recommended MEA, the system calculates:

   • Precedent count: How many agreements match the filters.
   • Markets with precedents: Which countries or market baskets have matching agreements.
   • Most recent year: The year of the newest matching agreement.
   • Outcome summary: The distribution of outcomes (e.g., access granted, restricted, withdrawn).

5. Review the summary table: The tool displays a summary table, showing each recommended MEA, its weighted precedent count, and a confidence signal.

   MEA	Precedents (weighted)	Confidence
   Outcome-guarantee	12 (8 weighted)	Well-precedented
   Population cost cap	7 (6 weighted)	Some precedent
   Coverage with evidence development	4 (4 weighted)	Some precedent
   Fixed rebate / discount	1 (1 weighted)	Limited precedent

   In this example, Outcome-guarantee is "Well-precedented" with 12 weighted precedents, indicating strong real-world validation. Fixed rebate / discount has "Limited precedent," suggesting it might be less common or require more justification.

6. Examine detailed case studies: You can then click on an MEA (e.g., Outcome-guarantee) to see a detailed table of up to 10 individual agreements, including product, country, payer, year, outcome measures, and status. This provides concrete examples to inform your strategy.

Common mistakes

• Skipping the precedent check: Relying solely on the scoring logic without validating against real-world usage can lead to proposing MEAs that are difficult to implement or negotiate in practice. Always consider the precedent layer for critical recommendations.
• Ignoring the confidence signal: A "Limited precedent" or "No precedent" signal isn't necessarily a deal-breaker, but it indicates a higher risk or novelty. Ignoring this signal means missing an opportunity to prepare stronger justifications or consider alternative MEAs.
• Not providing specific inputs: If you don't specify priority markets or therapeutic areas, the search might return less relevant global precedents, diluting the value of the validation.

Key takeaways

• The case study layer validates MEA recommendations by identifying real-world precedents from a library of past agreements.
• It requires additional inputs like priority markets and therapeutic area to refine the search.
• The system applies recency weighting, giving more importance to recent agreements.
• Results include a precedent count, markets, and an outcome summary for each MEA.
• A confidence signal (e.g., "Well-precedented," "Limited precedent") helps interpret the strength of real-world validation.