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In a review published in New England Journal of Medicine Evidence, Massimo Di Maio, MD, from the Department of Oncology at the University of Turin in Turin, Italy, and president-elect of the Italian Association of Medical Oncology, offers a detailed analysis of how patient-reported outcomes are used in clinical trials and explains the best practices for interpreting quality-of-life (QoL) data.

“QoL data are increasingly being included in oncology trials,” Di Maio told Univadis Italy, a Medscape Network platform. “However, the scientific community is less familiar with interpreting — or critically assessing — this type of data compared with more traditional endpoints like progression-free survival or overall survival.”

With growing support from scientific societies and regulatory agencies, QoL has become a standard endpoint in many studies. Yet practical guidance on its interpretation remains limited. Di Maio, who also served as lead author of the European Society For Medical Oncology guidelines on the use of patient-reported outcome measures in oncology, views this as a significant gap in the field.

“We must not forget that for many patients — especially those in advanced stages of disease — living well can be just as important, if not more so, than living longer,” he emphasized. He also cautioned that statistical significance does not always translate into meaningful clinical benefit.

From Trial Design…

To accurately assess QoL and maximize the value of the data, clinical trials must be appropriately designed from the outset. As outlined in the article, phase 3 randomized trials offer the ideal framework for QoL measurement. They allow investigators to compare results between treatment and control groups, tracking changes from baseline and over time.

Di Maio explains in the review that randomized trial designs help ensure that any differences observed between study arms are directly linked to the treatment itself.

Speaking with Univadis Italy, he also addressed the potential for bias in QoL data collection. “Some researchers argue that simply knowing they are receiving the experimental treatment may lead patients — perhaps even subconsciously — to perceive a benefit, which could influence patient-reported outcomes like QoL,” he said.

Still, Di Maio noted the difficulty in rigorously proving the existence or magnitude of such bias. Ideally, this would be tested by comparing blinded and open-label trials, but these studies are uncommon and logistically challenging. One coordinated analysis, led by Fabio Efficace, PhD, of the Italian Group for Adult Hematologic Diseases, found that trial blinding was not an independent predictor of QoL benefit. While the potential for bias cannot be dismissed, Di Maio believes its real-world impact is likely modest — and that open-label studies can still yield valid and meaningful QoL data.

…To Data Interpretation

Few oncology trials designate QoL as a primary endpoint, but this isn’t inherently a limitation, Di Maio noted. QoL can still be a valuable secondary endpoint — provided the results are interpreted carefully and in the proper context.

One of the central challenges is patient compliance with QoL questionnaires, which tends to be inconsistent. Factors contributing to low completion rates include limited time for clinicians to communicate the importance of QoL data, patient dropout due to disease progression or adverse outcomes, and technical issues such as failures in electronic data capture or patients’ unfamiliarity with digital tools. In such cases, Di Maio suggested that having a paper-based backup system can improve data reliability.

Ultimately, QoL should not be viewed as an optional or secondary concern, but as a fundamental part of assessing the true value of cancer therapies. Patient-reported outcome measures, while designed to capture quality of life, also offer critical insights into the broader effectiveness and utility of treatments.

“QoL data alone aren’t sufficient,” Di Maio emphasized, “but they are indispensable for understanding the value of a therapy — and for communicating that value clearly to patients.”

He concluded that interpreting QoL data accurately is not just a scientific imperative but also an ethical obligation to patients navigating serious and often life-limiting conditions.

This story was translated from Univadis Italy.