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Analysis of real-world progression and insufficient response variables and related endpoints among patients with non-Hodgkin lymphoma

Published

March 2025

Citation

Richey M, Fullerton C, Zhang Q, et al. Analysis of Real-World Progression and Insufficient Response Variables and Related Endpoints Among Patients with Non-Hodgkin Lymphoma. Advances in Therapy. 2025. https://link.springer.com/article/10.1007/s12325-025-03143-5

Overview

A persistent challenge in oncology RWE is the lack of standardized methods to assess disease progression and treatment response in EHR-derived data. Unlike clinical trials, where disease burden is measured through structured imaging assessments according to a trial protocol, real-world assessment is done based on clinical judgment of the treating clinician. This study tests an approach adapted from solid tumors  to use routine clinical documentation to define real-world progression (rwP) and real-world insufficient response (rwIR) variables and capture disease worsening and refractory disease events in patients with non-Hodgkin lymphoma (NHL). 

The study tested these measures in over 6,000 patients with NHL and found they were both reliable and useful for understanding treatment outcomes, validating the NHL-specific rwP/rwIR approach as feasible, reliable, and scalable. Specifically, researchers assessed the performance through inter-abstractor agreement on critical details, observing strong correlations between rwP/rwIR-based endpoints, such as real-world progression-free survival (rwPFS) and event-free survival (rwEFS), with overall survival (rwOS).

Why this matters

The ability to systematically capture these events from EHR data represents a major advancement in RWE methodology, allowing for a more robust understanding of the outcomes of patients with NHLs treated with routine clinical care and generating insights that can support regulatory submissions, comparative effectiveness research, and clinical decision-making. By adapting a previously validated approach used in solid tumors, the study demonstrates that these variables can be feasibly, reliably, and scalably extracted from EHR data.

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