Unsupervised item response theory models for assessing sample heterogeneity in patient-reported outcomes measures
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Published:2023-12-21
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Volume:
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ISSN:0962-9343
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Container-title:Quality of Life Research
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language:en
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Short-container-title:Qual Life Res
Author:
Sajobi Tolulope T.ORCID, Sanusi Ridwan A., Mayo Nancy E., Sawatzky Richard, Kongsgaard Nielsen Lene, Sebille Veronique, Liu Juxin, Bohm Eric, Awosoga Oluwagbohunmi, Norris Colleen M., Wilton Stephen B., James Matthew T., Lix Lisa M.
Abstract
Abstract
Purpose
Unsupervised item-response theory (IRT) models such as polytomous IRT based on recursive partitioning (IRTrees) and mixture IRT (MixIRT) models can be used to assess differential item functioning (DIF) in patient-reported outcome measures (PROMs) when the covariates associated with DIF are unknown a priori. This study examines the consistency of results for IRTrees and MixIRT models.
Methods
Data were from 4478 individuals in the Alberta Provincial Project on Outcome Assessment in Coronary Heart Disease registry who received cardiac angiography in Alberta, Canada, and completed the Hospital Anxiety and Depression Scale (HADS) depression subscale items. The partial credit model (PCM) based on recursive partitioning (PCTree) and mixture PCM (MixPCM) were used to identify covariates associated with differential response patterns to HADS depression subscale items. Model covariates included demographic and clinical characteristics.
Results
The median (interquartile range) age was 64.5(15.7) years, and 3522(78.5%) patients were male. The PCTree identified 4 terminal nodes (subgroups) defined by smoking status, age, and body mass index. A 3-class PCM fits the data well. The MixPCM latent classes were defined by age, disease indication, smoking status, comorbid diabetes, congestive heart failure, and chronic obstructive pulmonary disease.
Conclusion
PCTree and MixPCM were not consistent in detecting covariates associated with differential interpretations of PROM items. Future research will use computer simulations to assess these models’ Type I error and statistical power for identifying covariates associated with DIF.
Funder
Canadian Institutes of Health Research
Publisher
Springer Science and Business Media LLC
Subject
Public Health, Environmental and Occupational Health
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