Statistical Programming Support for Real World Evidence Outcomes Analysis in Rare Disease

Case Study Introduction

Transthyretin amyloidosis (ATTR) is a rare, progressive disease caused by misfolded transthyretin (TTR) protein depositing as amyloid in organs, most commonly affecting the heart and peripheral nerves.

This case study describes how Quanticate supported a top 10 large pharmaceutical company with running a global, multi-centre, real-world evidence, longitudinal observational programme designed to improve the understanding of ATTR natural history, variation in clinical presentation, and longer-term outcomes in diverse patient subgroups.

Objectives and Key Challenges

The sponsor needed a robust evidence-generation programme to address key knowledge gaps in ATTR:

Limited longitudinal data describing disease progression and outcomes across hereditary (mutation-associated) and wild-type ATTR.
High clinical variability driven by 130+ known TTR gene mutations, creating uncertainty in genotype–phenotype relationships and subgroup outcomes.
A lack of comprehensive data on regional and ethnic differences in disease expression and the genotype-phenotype relationship, contributing to significant scientific uncertainty.
Practical challenges in consistently characterising and monitoring disease over time across global sites, including reliance on evolving diagnostic approaches (for example, genotyping and cardiac imaging modalities).
While existing marketing approved treatments had shown promise in stabilizing TTR and slowing disease progression, long-term safety and efficacy data, especially in diverse patient populations, were still incomplete.
Complex, heterogeneous longitudinal datasets requiring advanced statistical modelling to produce credible, decision-ready insights.

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Strategic Approach and Tailored Solutions

Acting as the sponsor’s in-house real-world evidence experts, Quanticate’s team provided the following support for statistical programming analysis:

Longitudinal evidence frameworks which were designed for analysis-ready structures suitable for repeated measures and long-term follow-up, enabling consistent tracking of disease trajectories over time.
Strategies to evaluate outcomes across clinically meaningful subgroups (for example, genotype groups, geography/region, and baseline disease characteristics).
Advanced statistical modelling methods such as survival analysis and mixed-effects models to analysis complex heterogenous datasets, enabling quantify progression patterns, account for within-patient correlation, and support comparisons across subgroups.
Scalable programming workflows to support harmonised outputs from an international dataset and to enable reliable cross-region analyses.
Exploratory assessment of longer-term outcomes in patients receiving disease-modifying therapy and treatments, without referencing product names.

Successful Client Outcomes

Through Quanticate’s statistical programming and analytical support, the sponsor was able to:

Gain a clearer, evidence-based understanding of ATTR natural history, long-term disease progression, survival, and treatment-related outcome patterns across a broad global real-world population.
Improve confidence in interpreting how ATTR presentation and outcomes varied between hereditary and wild-type disease, mutation groups, regions, and baseline disease characteristics.
Reduce uncertainty around the real-world impact of disease-modifying therapies by strengthening evidence on longer-term safety and efficacy outcomes in patients with different genetic profiles and disease severities.
Improve the sponsor’s ability to characterise and communicate disease burden in cardiac amyloidosis, where progression and clinical impact can be difficult to quantify consistently.
Support evidence-led decision-making around patient management, future research priorities, and more personalised treatment strategies for people living with ATTR.

Conclusion

Quanticate enabled the sponsor to translate a complex global observational dataset into coherent, analysis-ready evidence that could be used for decision-making and reporting in ATTR. This support helped ensure the study outputs were robust, consistent, and suitable for use across stakeholders.

Statistical Programming Support for Real-World Evidence Long-Term Outcomes Analysis in Rare Disease

Case Study Introduction

Objectives and Key Challenges

Need support with a Real-world evidence study?

Strategic Approach and Tailored Solutions

Successful Client Outcomes

Conclusion

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