---
title: Why Hybrid Claims are a Game Changer for Rare Disease Research
description: Small cohort sizes and selection bias often stall rare disease studies. Learn how hybrid claims data provides a more stable, longitudinal view of the patient journey.
image: https://blog.forian.com/hubfs/Hybrid%20Data%20Rare%20Disease%20Blog%20%20(1).png
---

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 Mar 5, 2026 11:06:51 AM

# Why Hybrid Claims are a Game Changer for Rare Disease Research

![Picture of Wouter van der Pluijm](https://blog.forian.com/hs-fs/hubfs/WVDP%20Headshot_2023_centered.jpg?width=50&name=WVDP%20Headshot_2023_centered.jpg) [Wouter van der Pluijm](https://blog.forian.com/author/wouter-van-der-pluijm)

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Administrative insurance claims are the backbone of real-world evidence (RWE). However, for those of us working in the rare and ultra-rare disease space, traditional "closed" claims often feel like trying to view a landscape through a keyhole.

The bottleneck is the requirement for continuous enrollment. In most cases, losing a few patients due to a change in insurance is a rounding error. In rare diseases, it’s a data catastrophe. When we restrict a study only to patients who stay with a single payer, we aren't just losing numbers, we’re losing the full story.

[Hybrid claims data,](https://forian.com/chronos) which integrates both open and closed sources, offers a solution. By looking beyond the rigid boundaries of a single insurance plan, we can significantly boost cohort sizes and, more importantly, create evidence that actually reflects the real world.

*This blog is part of a series on hybrid claims data and real-world evidence. Read the first post* [*here*](https://blog.forian.com/hybrid-claims-data-essential-strategy-rwe-heor-studies)*.*

 

### **The Rare Disease Challenge: Small Numbers, High Stakes**

In the ultra-rare world, every patient is a data point we can't afford to lose. Yet, traditional methodology often acts as a sieve.

Take a recent use case involving a pediatric ultra-rare treatment. We started with **451** patients who had at least three paid claims for the therapy. But once we applied the standard "closed claims" filter, the group withered:

- Only **205** patients were even visible in the closed dataset.
- That dropped to **148** when requiring a year of history before treatment.
- By the time we required a year of data both before and after treatment, we were down to just **109**.

For an epidemiologist, this is where a study hits a wall. When your sample size is this small, statistical power evaporates. You can’t do meaningful subgroup analyses, and your confidence intervals become so wide they’re practically unusable for HEOR.

![Rare disease hybrid data blog image 1 (2)](https://blog.forian.com/hs-fs/hubfs/Rare%20disease%20hybrid%20data%20blog%20image%201%20(2).png?width=368&height=368&name=Rare%20disease%20hybrid%20data%20blog%20image%201%20(2).png)

## **Expanding Eligibility with Open Claims History**

Now, consider the shift when we introduce **open claims**. These follow the patient regardless of their payer, capturing the "breadcrumbs" of their journey across the healthcare system. In that same pediatric study, the numbers changed overnight:

- **394** patients were identifiable in open claims.
- **159** patients maintained 12 months of pre- and post-index activity.
- Crucially, **73 patients** appeared in the open data who were completely invisible to the closed claims system.

By combining these into a **hybrid cohort**, the number of viable patients for a full longitudinal study jumped from **109 to 220**. We didn’t just "add more rows" to a spreadsheet; we doubled the statistical power of the study.

## ![Hybrid Claims Data Blog Image 2 (1)](https://blog.forian.com/hs-fs/hubfs/Hybrid%20Claims%20Data%20Blog%20Image%202%20(1).png?width=427&height=427&name=Hybrid%20Claims%20Data%20Blog%20Image%202%20(1).png)

## **Why "More" Also Means "Better"**

This isn't just a numbers game. It’s about **generalizability**. Closed claims are inherently biased toward "stable" patients. They change jobs, switch from commercial insurance to Medicaid, or age into different coverage tiers.

When we only study the patients who *stay put*, we ignore the high-need populations who experience coverage gaps or provider transitions. Hybrid data reduces this selection bias, offering:

**1. Broader Geography:** Capturing patients in regions where a single payer might not be dominant.  
**2. Diverse Payer Mix:** A more accurate reflection of the commercial, Medicaid, and Medicare split.  
**3. Realistic Patient Journeys:** Capturing the diagnostic odyssey and specialist hops that define the rare disease experience.

## **Statistical Power: More Than a Numbers Game**

Increasing sample size does three critical things:

1. Improves the precision of treatment effect estimates
2. Enables subgroup analyses (age bands, severity, line of therapy)
3. Supports longer follow-up windows

In rare disease HEOR studies, longitudinal depth is often as important as cohort size.

In the ultra-rare example, hybrid integration enabled more patients to meet longitudinal criteria, strengthening outcome measurement credibility and the overall defensibility of the evidence. This creates insights that are more useful for long-term decision making.

The ability to say, “This analysis reflects a broader, more representative treated population” is powerful.

 

**Moving from Incremental to Foundational**

For biopharma teams, the implications are practical. A study that looks "underpowered" on paper using traditional data might actually be viable in a hybrid environment. More importantly, the evidence becomes more defensible. When you present a value dossier to a regulator or a payer, being able to say your data represents the actual treated population, not just a sliver of one insurer's database, is a massive advantage.

The industry is moving past the era where closed claims alone are enough. In rare disease research, where the patient count is the ultimate constraint, hybrid integration isn't just a luxury. It’s the new standard for building evidence that sticks.

[Data](https://blog.forian.com/tag/data), [Hybrid Data](https://blog.forian.com/tag/hybrid-data), [RWD](https://blog.forian.com/tag/rwd), [RWE](https://blog.forian.com/tag/rwe), [Rare Diseases](https://blog.forian.com/tag/rare-diseases)

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 May 7, 2026 1:34:01 PM

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 Oct 21, 2025 11:30:15 AM

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