Harmonizing patient-reported outcome measures for nasal complaints using traditional and machine learning methods

Key Points

• The study demonstrates that machine learning methods enhance the quality of data harmonization.
• Results indicate a stronger potential for machine learning compared to traditional methods like linear equating.
• Analysis includes both machine learning and traditional statistical methods such as Item Response Theory (IRT).
• Findings suggest improved data harmonization may lead to better patient-reported outcomes in clinical settings.