Unsupervised item response theory models for assessing sample heterogeneity in patient-reported outc...
Unsupervised item response theory models for assessing sample heterogeneity in patient-reported outcomes measures
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Publisher
Cham: Springer International Publishing
Journal title
Language
English
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Publisher
Cham: Springer International Publishing
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Contents
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 consis...
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Full title
Unsupervised item response theory models for assessing sample heterogeneity in patient-reported outcomes measures
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Record Identifier
TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_10894181
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_10894181
Other Identifiers
ISSN
0962-9343
E-ISSN
1573-2649
DOI
10.1007/s11136-023-03560-5
