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The Guttman errors as a tool for response shift detection at subgroup and item levels
Myriam Blanchin1, Véronique Sébille2, Alice Guilleux2
1EA 4275, Biostatistics, Pharmacoepidemiology and Subjective Measures in Health Sciences, University of Nantes, Nantes, France. myriam.blanchin@univ-nantes.fr.
Summary
Guttman errors identify response shift in patient-reported outcomes, aiding interpretation of changes. This method effectively distinguishes individuals with response shift from those without.
Area of Science:
- Psychometrics
- Health Outcomes Research
Background:
- Interpreting changes in patient-reported outcome (PRO) data requires robust statistical methods.
- Identifying response shift (RS) is crucial for accurate PRO data analysis.
- Guttman errors (GE) offer a potential method for detecting response shift by identifying response discrepancies.
Purpose of the Study:
- To explore the utility of a Guttman error-based method for detecting response shift at subgroup and item levels.
- To assess the benefits of using GE for identifying discrepancies in respondent answers.
- To identify patient subgroups more likely to exhibit response shift.
Main Methods:
- Analysis of the SatisQoL study data.
- Calculation of Guttman errors (GE) for individuals at baseline (T0) and 6 months post-discharge (M6).
- Application of the RespOnse Shift ALgorithm in Item response theory (ROSALI) to identify response shift based on GE patterns.
Main Results:
- Patients with a high number of GE at M6 (but not T0) were identified as potentially experiencing RS.
- Patients with consistently low GE at both time points showed no evidence of RS.
- Different types of RS, including non-uniform recalibration and reprioritization, were more prevalent in the high-GE group.
Conclusions:
- Guttman errors are valuable for detecting response shift in PRO data.
- Item response theory models, combined with Guttman errors, can effectively discriminate individuals with and without response shift at the item level.
- This approach enhances the interpretability of changes in PRO measures.
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