Evidence map›Paper›PMID 42522156›Full record

ReviewClinical and translational science2026

Tutorial on Analyzing Patient-Reported Outcome Measures in Clinical Development.

Krina Mehta, Jim H Hughes, Jiawei Zhou, Benyam Muluneh, Ari Gnanasakthy

Abstract readReview
In one paragraph

Review in Clinical and translational science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Krina MehtaNAMLink, Cypress, CA, USA.ORCID 0000-0002-7621-6436
Jim H HughesPfizer Inc., Groton, Connecticut, USA.ORCID 0000-0002-1862-5805
Jiawei ZhouEshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina, USA.ORCID 0009-0000-3317-0311
Benyam MulunehEshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina, USA.ORCID 0000-0002-2387-6676
Ari GnanasakthyRTI Health Solutions, Research Triangle Institute, Durham, North Carolina, USA.ORCID 0000-0001-9176-3983

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Drug DevelopmentPatient Reported Outcome MeasuresClinical Decision-MakingHumansbounded item score modelitem‐response theorypatient‐reported outcomespharmacometrics

Identifiers

PMID42522156
PMCPMC13415975

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.