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Quality of Life Research

, Volume 24, Issue 3, pp 553–564 | Cite as

RespOnse Shift ALgorithm in Item response theory (ROSALI) for response shift detection with missing data in longitudinal patient-reported outcome studies

  • Alice Guilleux
  • Myriam Blanchin
  • Antoine Vanier
  • Francis Guillemin
  • Bruno Falissard
  • Carolyn E. Schwartz
  • Jean-Benoit Hardouin
  • Véronique SébilleEmail author
Response Shift and Missing Data

Abstract

Purpose

Some IRT models have the advantage of being robust to missing data and thus can be used with complete data as well as different patterns of missing data (informative or not). The purpose of this paper was to develop an algorithm for response shift (RS) detection using IRT models allowing for non-uniform and uniform recalibration, reprioritization RS recognition and true change estimation with these forms of RS taken into consideration if appropriate.

Methods

The algorithm is described, and its implementation is shown and compared to Oort’s structural equation modeling (SEM) procedure using data from a clinical study assessing health-related quality of life in 669 hospitalized patients with chronic conditions.

Results

The results were quite different for the two methods. Both showed that some items of the SF-36 General Health subscale were affected by response shift, but those items usually differed between IRT and SEM. The IRT algorithm found evidence of small recalibration and reprioritization effects, whereas SEM mostly found evidence of small recalibration effects.

Conclusion

An algorithm has been developed for response shift analyses using IRT models and allows the investigation of non-uniform and uniform recalibration as well as reprioritization. Differences in RS detection between IRT and SEM may be due to differences between the two methods in handling missing data. However, one cannot conclude on the differences between IRT and SEM based on a single application on a dataset since the underlying truth is unknown. A next step would be to implement a simulation study to investigate those differences.

Keywords

Item response theory Response shift Missing data Attrition Bias Quality of life 

Notes

Acknowledgments

This study was supported by the Institut National du Cancer, under reference “INCA_6931.” The SatisQoL cohort project (investigators: P. Auquier, F. Guillemin (PI), M. Mercier) was supported by an IRESP (Institut de recherche en santé publique) Grant from Inserm, and a PHRC (Programme Hospitalier de Recherche Clinique) National Grant from French Ministry of Health, France.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Alice Guilleux
    • 1
  • Myriam Blanchin
    • 1
  • Antoine Vanier
    • 1
    • 2
    • 3
  • Francis Guillemin
    • 4
  • Bruno Falissard
    • 5
    • 6
  • Carolyn E. Schwartz
    • 7
    • 8
  • Jean-Benoit Hardouin
    • 1
    • 9
  • Véronique Sébille
    • 1
    • 9
    Email author
  1. 1.Biostatistics, Pharmacoepidemiology and Subjective Measures in Health Sciences, EA 4275University of NantesNantesFrance
  2. 2.Department of BiostatisticsUPMC, Univ. Paris 06ParisFrance
  3. 3.Department of Biostatistics Public Health and Medical Informatics, AP-HPUniversity Hospitals Pitié-Salpêtrière Charles-FoixParisFrance
  4. 4.EA 4360 Apemac, Lorraine University, Paris Descartes UniversityNancyFrance
  5. 5.INSERM 669, Université Paris-Sud, Université Paris DescartesParisFrance
  6. 6.Département de santé publique, AP-HPHôpital Paul BrousseVillejuifFrance
  7. 7.DeltaQuest Foundation, Inc.ConcordUSA
  8. 8.Department of Medicine and Orthopeadic SurgeryTufts University School of MedicineBostonUSA
  9. 9.Clinical Research Unit, Department of Methodology and BiostatisticsUniversity Hospital of NantesNantesFrance

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