Unsupervised item response theory models for assessing sample heterogeneity in patient-reported outcomes measures.

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 identi

SUBMITTER: Sajobi TT 

PROVIDER: S-EPMC10894181 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature