Evidence map›Paper›PMID 40681959›Full record

ArticleQuality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation2025

Tree-based latent variable model for assessing differential item functioning in patient-reported outcome measures: a simulation study.

Olayinka I Arimoro, Lisa M Lix, Scott B Patten, Rick Sawatzky, Veronique Sebille, Juxin Liu, Samuel Wiebe, Colin B Josephson, Tolulope T Sajobi

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Article in Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1 · What the graph read from it

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Olayinka I ArimoroDepartment of Community Health Sciences & O'Brien Institute for Public Health, University of Calgary, Calgary, AB, Canada.
Lisa M LixDepartment of Community Health Sciences, University of Manitoba, Winnipeg, MB, Canada.
Scott B PattenDepartment of Community Health Sciences & O'Brien Institute for Public Health, University of Calgary, Calgary, AB, Canada.
Rick SawatzkySchool of Nursing, Trinity Western University, Langley, BC, Canada.
Veronique SebilleSPHERE, Nantes Université, Université de Tours, INSERM, MethodS in Patient-Centered Outcomes and Health Research, Nantes, France.
Juxin LiuDepartment of Mathematics & Statistics, University of Saskatchewan, Saskatoon, Canada.
Samuel WiebeDepartment of Community Health Sciences & O'Brien Institute for Public Health, University of Calgary, Calgary, AB, Canada.
Colin B JosephsonDepartment of Community Health Sciences & O'Brien Institute for Public Health, University of Calgary, Calgary, AB, Canada.
Tolulope T SajobiDepartment of Community Health Sciences & O'Brien Institute for Public Health, University of Calgary, Calgary, AB, Canada. ttsajobi@ucalgary.ca.ORCID http://orcid.org/0000-0002-5696-5552

Funding

CIHR 438908
6 · The paper itself

Abstract

purposeThe validity of patient-reported outcome measures (PROMs) can be threatened by heterogeneity in how patients with the same underlying health status interpret and respond to questions about their health, a phenomenon known as differential item functioning (DIF). Although tree-based latent variable models, such as the partial credit model based on recursive partitioning (PCTree), are used to test for DIF, their performance has not been comprehensively investigated. We evaluated the statistical properties of PCTree to test for DIF in polytomous-scored PROM items.

methodsComputer simulations for a variety of data-analytic conditions were used to evaluate the performance of PCTree with and without Bonferroni adjustments. The performance of this model was assessed using Type I error and statistical power rates. The robustness of PCTree with respect to control of the familywise Type I error rate was evaluated using Bradley's liberal criterion; error rates within the interval of 2.5-7.5% for α = 5.0% indicate the method is robust.

resultsUsing Bradley's criterion, PCTree with a Bonferroni correction provided good control of familywise Type I error rate across all simulation conditions. The average statistical power rate of PCTree with Bonferroni correction was at least 80% when N

conclusionsPCTree is promising for evaluating DIF in potentially heterogeneous populations. We provide recommendations about data analytic conditions for using the model.

Indexed as

Models, StatisticalPatient Reported Outcome MeasuresComputer SimulationHumansPsychometricsQuality of LifeReproducibility of ResultsSurveys and QuestionnairesDifferential item functioningLatent variable modelPartial credit modelPatient-reported outcome measuresStatistical powerType I error

Identifiers

PMID40681959

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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.