Evidence map›Paper›PMID 26694962›Full record

ArticleQuality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation2016

Using structural equation modeling to detect response shifts and true change in discrete variables: an application to the items of the SF-36.

Mathilde G E Verdam, Frans J Oort, Mirjam A G Sprangers

Open access · hybridAbstract read
In one paragraph

Article in Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed, 1 pooled it
3.6field-weighted citation impact, top 7% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

23 citing papers in PubMed, 1 synthesis or guideline pooled it, 42 citations in OpenAlex.

  1. Pooled it
  2. Trial
  3. Article
  4. Response shift-adjusted longitudinal assessment of patient-reported outcomes in older people living with HIV: a multilevel structural equation and Rasch analysis.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2026
    Article
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  7. Response shift in parent-reported psychopathology in children with chronic physical illness.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2023
    Article
  8. Article
  9. Evaluation of the link between the Guttman errors and response shift at the individual level.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2022
    Article
  10. Using structural equation modeling to investigate change and response shift in patient-reported outcomes: practical considerations and recommendations.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2021
    Article
  11. Reply to letter to editor: "RespOnse Shift ALgorithm in item response theory (ROSALI) for response shift detection with missing data in longitudinal patient-reported outcome studies".Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2020
    Article
  12. How peoples' ratings of dental implant treatment change over time?Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2020
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  13. Article
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  16. Subjectively different but objectively the same? Three profiles of QoL in people with severe mental health problems.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2018
    Article
  17. Article
  18. Scoping review of response shift methods: current reporting practices and recommendations.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2018
    Article
  19. Item bias detection in the Hospital Anxiety and Depression Scale using structural equation modeling: comparison with other item bias detection methods.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2017
    Article
  20. Minimal impact of response shift for SF-12 mental and physical health status in homeless and vulnerably housed individuals: an item-level multi-group analysis.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2017
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 1 institution in 1 country.

Mathilde G E VerdamDepartment of Medical Psychology, Academic Medical Centre, University of Amsterdam, Amsterdam, The Netherlands. m.g.e.verdam@uva.nl.
Frans J OortDepartment of Medical Psychology, Academic Medical Centre, University of Amsterdam, Amsterdam, The Netherlands.
Mirjam A G SprangersDepartment of Medical Psychology, Academic Medical Centre, University of Amsterdam, Amsterdam, The Netherlands.
University of Amsterdam · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThe structural equation modeling (SEM) approach for detection of response shift (Oort in Qual Life Res 14:587-598, 2005. doi: 10.1007/s11136-004-0830-y ) is especially suited for continuous data, e.g., questionnaire scales. The present objective is to explain how the SEM approach can be applied to discrete data and to illustrate response shift detection in items measuring health-related quality of life (HRQL) of cancer patients.

methodsThe SEM approach for discrete data includes two stages: (1) establishing a model of underlying continuous variables that represent the observed discrete variables, (2) using these underlying continuous variables to establish a common factor model for the detection of response shift and to assess true change. The proposed SEM approach was illustrated with data of 485 cancer patients whose HRQL was measured with the SF-36, before and after start of antineoplastic treatment.

resultsResponse shift effects were detected in items of the subscales mental health, physical functioning, role limitations due to physical health, and bodily pain. Recalibration response shifts indicated that patients experienced relatively fewer limitations with "bathing or dressing yourself" (effect size d = 0.51) and less "nervousness" (d = 0.30), but more "pain" (d = -0.23) and less "happiness" (d = -0.16) after antineoplastic treatment as compared to the other symptoms of the same subscale. Overall, patients' mental health improved, while their physical health, vitality, and social functioning deteriorated. No change was found for the other subscales of the SF-36.

conclusionThe proposed SEM approach to discrete data enables response shift detection at the item level. This will lead to a better understanding of the response shift phenomena at the item level and therefore enhances interpretation of change in the area of HRQL.

Indexed as

Health SurveysModels, TheoreticalSickness Impact ProfileAgedAnxietyFemaleHumansMaleMental HealthMiddle AgedNeoplasmsPainQuality of LifeSelf CareDiscrete dataHealth-related quality of life (HRQL)Item-level analysesResponse shiftSF-36 health surveyStructural equation modeling (SEM)

Identifiers

PMID26694962
PMCPMC4870306
OpenAlexW2297410916

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.