Learning insights: advancing health technology assessment with blended survival analysis

Why do we need blended survival analysis?

Blended survival analysis represents a significant advancement in health technology assessment (HTA) by combining clinical trial data with real-world evidence (RWE) to improve the accuracy of long-term survival estimates. Traditional survival analysis often falls short, as it relies heavily on extrapolating clinical trial data, which typically covers only 40% of the model time horizon, potentially leading to unrealistic survival projections. Blended survival analysis addresses this limitation by incorporating RWE, which provides a more comprehensive view of patient outcomes beyond the controlled environment of clinical trials.

The observed and external survival curves are combined to produce a more robust estimate of long-term survival

Benefits and Challenges of Blended Survival Analysis

Benefits:

1. Enhanced Accuracy: By integrating RWE, blended survival analysis produces more realistic survival estimates that align better with clinical expectations. This is crucial for making informed decisions in HTA, especially when clinical trial follow-up is limited.
2. Longer Follow-Up: RWE offers extended follow-up periods compared to clinical trials, capturing patient outcomes over a longer duration and providing insights into the long-term effectiveness of treatments.
3. Real-World Applicability: Data from real-world settings reflect patient experiences outside the controlled conditions of clinical trials, offering a more accurate representation of how treatments perform in everyday clinical practice.
4. Reduced Uncertainty: Incorporating RWE reduces the uncertainty inherent in relying solely on clinical trial data, leading to more robust cost-effectiveness analyses and better-informed healthcare decisions.

Challenges:

1. Data Quality and Availability: The reliability of blended survival analysis heavily depends on the quality and comparability of the RWE. Discrepancies between clinical trial populations and real-world cohorts can skew results, necessitating careful selection and validation of RWE sources.
2. Methodological Variability: The lack of standardized guidelines for incorporating RWE into survival analysis poses a challenge. Currently, methodologies are evolving, and HTA bodies like NICE have yet to establish specific protocols, leading to variability in application and acceptance.
3. Reliance on Clinical Opinion: In the absence of comprehensive patient-level data, clinical expert opinions are often used to construct RWE curves. This approach, while valuable, introduces subjectivity and potential bias into the analysis.
4. Complexity in Justification: HTA submissions utilizing blended survival analysis require detailed methodological justifications. This includes explaining the choice of data sources, blending techniques, and the assumptions made, which can be labor-intensive and complex.

If you are interested in Blended Survival Analysis, we can help you navigate your way to maximising the probability of HTA success. Discuss your needs with us and see how we can boost your product launch:

https://www.fiecon.com/landing/contact

? US: Karl Freemyer

? UK: Lawrence Murphy

Check out the key learnings from the first part of the 2nd FIEConference 2024 in the following article:

About FIEConference

The FIEConference is a quarterly seminar focused on HEOR and Access, designed to boost the technical skills and efficiency of our operations team. It highlights case studies and the real-world application of technical skills. This series aims to enhance technical proficiency and confidence in addressing new methodological challenges while promoting greater inter-project collaboration and efficiency.