Tutorial on Analyzing Patient‐Reported Outcome Measures in Clinical Development
Year: 2026
Type: article
Abstract: Patient-reported outcomes (PROs) are now considered essential components of clinical development and regulatory assessment. Despite their widespread collection, PRO data remain under-utilized, with analyses often limited to descriptive summaries or single time-point comparisons that fail to capture the richness, longitudinal structure, and patient-centric meaning rooted within these measures. In this tutorial, we have outlined key considerations for aligning fit-for-purpose PRO measures with clearly articulated research questions and estimands, and applying analytically robust methods that respect the bounded, noisy, and often incomplete nature of PRO data. Through working examples, we demonstrated how pharmacometric approaches, such as bounded outcome score modeling and item response theory-nonlinear mixed effects modeling, can enable more efficient use of all available data, improve sensitivity to treatment effects, and provide interpretable longitudinal trajectories that are directly relevant to clinical decision-making. Importantly, pharmacometric approaches allow PROs to move beyond exploratory endpoints to becoming integral tools for understanding treatment tolerability, differentiating therapies with similar efficacy, and predicting patient-centric treatment outcomes. Looking forward, realizing the full potential of PROs will require early integration of patient-reported outcome measures (PROMs) into clinical development programs, careful consideration of assessment timing and frequency, and continued collaboration among clinicians, statisticians, pharmacometricians, and regulators. Ultimately, integrating PROs in drug development as one of the clinical outcome assessments is a necessary step toward ensuring that drug development and clinical decision-making more accurately reflect what matters most to patients: how they feel, how they function, and how treatments affect their quality of life.
Source: UNC Libraries
DOI: 10.17615/b89g-gs86
Institution: University of North Carolina at Chapel Hill
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7201863304