Characterizing a real-world response variable and associated endpoint in oncology

Summary of: Ma, X., Bellomo, L., Magee, K. et al. Characterization of a Real-World Response Variable and Comparison with RECIST-Based Response Rates from Clinical Trials in Advanced NSCLC. Adv Ther (2021).

Our summary

Treatment effectiveness metrics for real-world research, similar to those assessed in clinical trials, are needed in order to generate interpretable RWE fit to support decision making during clinical development or regulatory processes.

Flatiron researchers set out to develop a real-world response (rwR) variable by capturing the clinician’s interpretation of radiographic assessments of solid tumor burden from information contained in EHRs and to evaluate its clinical relevance.

This study shows that EHR data curation can be leveraged to generate a feasible and reliable rwR variable, and associated endpoint (real-world response rate (rwRR)) analyses show that this variable is clinically meaningful. This type of research can help to fully realize the potential value of EHR-derived data.

 

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Why this matters

In oncology research, understanding the proportion of patients that experience tumor response (i.e., the response rate) after treatment is an important metric of therapeutic benefit. While correlation with survival benefit may vary, a response rate endpoint can support preliminary clinical development decisions or accelerated regulatory approvals. As such, a method to ascertain responses in real-world cohorts of patients with solid tumors would be a valuable tool for the utilization of RWE in clinical development or regulatory programs.

This study describes an approach to generate a response variable derived from information contained in EHRs, which enables analyses of real-world response rates. For researchers seeking to contextualize the meaning of this variable and associated endpoint, this report also presents initial work regarding the correlations with other clinical outcomes and with data obtained in clinical studies. This is an initial step for the incorporation of response analyses in real-world studies.

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