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Rasch analysis in the development of a simplified version of the national eye institute visual-function questionnaire-25 for utility estimation

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Abstract

Purpose

Preference-based health measures value how people feel about the desirability of a health state. Generic measures may not effectively capture the impact of vision loss from ocular diseases. Disease-targeted measures could address this limitation. This study developed a vision-targeted health state classification system based on the National Eye Institute Visual Function Questionnaire-25 (NEI VFQ-25).

Methods

Secondary analysis of NEI VFQ-25 data from studies of patients with central (n = 932)- and peripheral-vision loss (n = 2,451) were used to develop a health state classification system. Classical test theory and Rasch analyses were used to identify a smaller set of NEI VFQ-25 items suitable for the central- and peripheral-vision-loss groups.

Results

Rasch analysis of the NEI VFQ-25 items using the peripheral vision–loss data indicated that 11 items fit a unidimensional model, while 14 NEI VFQ-25 items fit using the central-vision-loss data. Combining peripheral-vision-loss data and central-vision-loss data resulted in 9 items fitting a unidimensional model. Six items covering near vision, distance vision, social vision, role difficulties, vision dependency, and vision-related mental health were selected for the health-state classification.

Conclusions

The derived health-state classification system covers relevant domains of vision-related functioning and well-being.

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Abbreviations

AMD:

Age-related macular degeneration

DIF:

Differential item functioning

EQ-5D:

EuroQol

HRQL:

Health-related quality of life

HUI:

Health utility index

NEI VFQ-25:

National eye institute visual function questionnaire-25

NICE:

National institute of health and clinical excellence

QALY:

Quality-adjusted life year

QWB-SA:

Quality of well-being, self-administered

SF-12:

The 12-item short form health survey

SF-36:

Medical outcome short form (32) health survey

VFQ-UI:

Visual function questionnaire—utility index

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Acknowledgments

Authors would like to thank the following researchers for providing data for this study: Rhett Schiffman, MD, MS, MHSA, Allergan, Inc., Irvine, California Rohit Varma, MD, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California The authors would also like to thank Maria Stoeckl Mattera and Beenish Nafees for their assistance with this study. This research was supported by Allergan Inc., Irvine, California.

Author information

Authors and Affiliations

  1. Global Health Outcomes Strategy and Research, Allergan Inc, 2525 Dupont Drive, Irvine, CA, 92612, USA

    Jonathan W. Kowalski

  2. United BioSource Corporation, Bethesda, MD, USA

    Anne M. Rentz, Wen-Hung Chen & Dennis A. Revicki

  3. Medical Affairs, Allergan Inc, 2525 Dupont Drive, Irvine, CA, 92612, USA

    John G. Walt

  4. Oxford Outcomes, Oxford, UK

    Andrew Lloyd

  5. Payer and Health Outcomes Regional Scientific Services, Allergan Inc, 2525 Dupont Drive, Irvine, CA, 92612, USA

    Jeff Lee

  6. University of Sheffield, Sheffield, UK

    Tracey A. Young & John E. Brazier

  7. Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Neil M. Bressler

  8. Duke University, Durham, NC, USA

    Paul Lee

  9. University of California-Los Angeles, Los Angeles, CA, USA

    Ron D. Hays

Authors
  1. Jonathan W. Kowalski
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  2. Anne M. Rentz
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  3. John G. Walt
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  4. Andrew Lloyd
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  5. Jeff Lee
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  6. Tracey A. Young
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  7. Wen-Hung Chen
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  8. Neil M. Bressler
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  9. Paul Lee
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  10. John E. Brazier
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  12. Dennis A. Revicki
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Correspondence to Jonathan W. Kowalski.

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Kowalski, J.W., Rentz, A.M., Walt, J.G. et al. Rasch analysis in the development of a simplified version of the national eye institute visual-function questionnaire-25 for utility estimation. Qual Life Res 21, 323–334 (2012). https://doi.org/10.1007/s11136-011-9938-z

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