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In recent years, external control arms (ECAs) are being increasingly used in clinical research, particularly in cases where randomized controlled trials (RCTs) are not feasible or appropriate. Real-world data (RWD) from patient registries, electronic health records (EHRs), and other observational sources are the basis of ECAs, thus offering critical reference points for single-arm studies.(1, 2) However, they can significantly impact the validity of ECAs due to the risk of unmeasured confounding and systematic biases.(3) ECAs are not randomized like RCTs, which makes them fundamentally susceptible to bias owing to the differences in patient characteristics, treatment practices, and methods of data collection.(4) To address this challenge, quantitative bias analysis (QBA) can be a beneficial methodological tool.(5)

QBA enables researchers to systematically model and calculate the possible impact of unmeasured confounding on treatment effect estimates. Instead of solely depending on conventional sensitivity analyses or assuming that statistical adjustments for measured variables are adequate, QBA offers a systematic approach to estimate how much an unobserved confounder would require to control both treatment and outcome to justify an observed treatment effect. This quantitative perspective adds refinement and transparency to studies involving ECA, particularly in cases where the severity and direction of potential bias is assessed or bounded. The increasing realization of QBA’s applicability has persuaded its implementation in regulatory and health technology assessment (HTA) scenarios, where decision-makers need robust, reliable evidence for decisions on policy and reimbursement.(5, 6)

The Q-BASEL study (Quantitative Bias Analysis in External Control Arms for Standardization and Evidence Level) is a crucial milestone in depicting the utility of QBA in practice. This study comprehensively employed QBA techniques across several real-world ECA cases to estimate the possible impact of unmeasured confounding on the observed treatment effects. By incorporating probabilistic bias analysis and deterministic sensitivity states, the researchers were able to measure the strength of the findings from these ECAs. The results of the Q-BASEL study emphasized that even moderate confounding could drastically change the understanding of treatment benefit, while also exhibiting robustness of observed treatment effect under reasonable bias assumptions in several cases. This strengthened the value of QBA as both a diagnostic method and a reliability enhancer for ECA-based evidence.(6)

Notably, the Q-BASEL study encourages a mindset shift, from trying to remove all bias to explicitly measuring it and putting its effect into perspective. Embracing the inherent uncertainty of RWD empowers researchers and reviewers to make more informed decisions. The study also highlights the need for transparency in assumptions, appropriate documentation of parameter scales, and stakeholder communication, all of which are vital for QBA to significantly help in decision-making. Additionally, its impact extends beyond oncology and rare diseases, two fields with frequent adoption of ECAs, to larger treatment landscapes with rising RWD integration.(6)

With increasing use of ECAs in regulatory and HTA decisions, QBA offers a transparent, scientific method to evaluate whether treatment effect estimates may be affected by bias rather than depicting a real clinical benefit. The Q-BASEL study shows how this can be assessed with practical and reproducible approaches. With rising expectations for transparency and methodological robustness, integrating QBA into RWE studies can potentially become standard practice.

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References

1. Sosinsky AZ, Parzynski CS, Casso D. The Role of External Control Arms in Drug Development and Considerations for Success. ISPOR – Value & Outcomes Spotlight. 2024; 10(4).
2. Mishra-Kalyani PS, Amiri Kordestani L, Rivera DR, et al. External control arms in oncology: current use and future directions. Ann Oncol. 2022; 33(4):376-383.
3. Quantifying Bias in Real-World Studies: A New Hope for RWD Acceptance or Are HTAers Gonna Hate? ISPOR. 2022. [Accessed online on 10^th July 2025]. Available at: https://www.ispor.org/docs/default-source/euro2022/ispor-eu-2022—qbasel-symposium—v1.pdf?sfvrsn=d14165ea_0
4. Khachatryan A, Read SH, Madison T. External control arms for rare diseases: building a body of supporting evidence. J Pharmacokinet Pharmacodyn. 2023; 50:501-506.
5. Thorlund K, Duffield S, Popat S, et al. Quantitative bias analysis for external control arms using real-world data in clinical trials: a primer for clinical researchers. J Comp Eff Res. 2024; 13(3):e230147.
6. Gupta A, Hsu G, Kent S, et al. Quantitative Bias Analysis for Single-Arm Trials With External Control Arms. JAMA Netw Open. 2025; 8(3):e252152.