Bias in Real-world Endpoints when Conducting External Control Arm Analyses

Background

When augmenting single-arm trials with real-world data (RWD) for external control arm analyses, biases may arise due to differences in how and when patients’ disease progression is assessed between the two settings. This is particularly worrisome in oncology studies, where time-to-event endpoints like progression-free survival (PFS) rely on strict progression evaluation criteria collected at fixed assessment schedules. On the other hand, RWD assessments may be less frequent or regimented, and determination of disease progression may be made based solely on unstructured clinical documentation or more flexible guidelines than the trial standards.

Ackerman et al (2026) "Quantitative bias analyses to address measurement error in time-to-event endpoints." American Journal of Epidemiology.

These types of endpoint measurement error may cause bias (misclassification and surveillance bias), such that real-world PFS estimates appear longer (or shorter) than expected under trial settings, thus limiting the ability to make apples-to-apples comparisons between a single-arm trial and its real-world comparator arm.

Key Findings and Takeaways

It is often infeasible in ECA analyses to construct RWD comparators with endpoints aligned to trial standards, but:

  • Simulation studies can demonstrate when no bias is present due to measurement misalignments.
  • Methods can be applied to quantify and reduce statistically meaningful measurement error bias.
  • Quantitative Bias Analyses can contextualize biased ECA findings when measurement error bias is not directly estimable.

Robust statistical methods can adequately align RWD endpoints towards trial standards and reduce biases for regulatory approval of ECAs.

Relevant Papers

Ackerman, B., Gan, R.W., Meyer, C.S., Zhang, Y., Wang, J.R., Hayden, J., et al. (2026). Quantitative bias analyses to address measurement error in time-to-event endpoints. American Journal of Epidemiology.

Edwards, J.K., Cole, S.R., Zivich, P.N., Ackerman, B., Napravnik, S., Henderson, H., Lash, T.L., Shook-Sa, B.E. (2026). Risk functions with outcome measurement error. Biostatistics.

Ackerman, B., Gan, R.W., Zhang, Y., Siddique, J., Roose, J., Lund, J.L., et al. (2025). Regression calibration for time-to-event outcomes: Mitigating bias due to measurement error in real-world endpoints. Epidemiologic Methods.

Ackerman, B., Gan, R.W., Meyer, C.S., Wang, J.R., Zhang, Y., Hayden, J., et al. (2024). Measurement error and bias in real‑world oncology endpoints when constructing external control arms. Frontiers in Drug Safety and Regulation.

This work was presented at the 2025 EMA Workshop on the use of external controls for evidence generation in regulatory decision-making