Real-world data to support clinical evidence generation in oncology: considerations for biomarker-based cohort selection, end point assessment and other methodological issues

Randomized clinical trials remain the evidentiary gold standard for oncology, but they are often insufficient to fully characterize benefit-risk in rare cancers, biomarker-defined subgroups, and other settings where trial evidence is limited. As precision oncology advances, real-world data and real-world evidence (RWE) offer an increasingly important opportunity to address evidentiary gaps outside traditional trial populations. Yet the use of RWE in oncology requires more than access to data; it requires fit-for-purpose methods that account for the distinctive challenges of biomarker-based cohort selection, end point definition and bias mitigation. This paper reviews methodological considerations for oncology RWE generation and synthesizes selected use cases to illustrate their application in practice. We highlight unique issues arising in oncology RWE research, including incomplete or delayed biomarker ascertainment, heterogeneity in testing platforms and thresholds, misclassification of molecularly defined cohorts, variability in line-of-therapy assignment and time-related biases that can compromise interpretability. We also examine real-world end point ascertainment and discuss considerations for assessing end point validity, completeness and translatability. In addition, we outline how common oncology clinical trial end points can be operationalized as approximate real-world constructs, recognizing that differences in assessment schedule, measurement method and available data may affect comparability and performance. By integrating these methodological challenges with practical design considerations, this paper provides timely guidance to support generation of credible RWE for clinical development in oncology.