Skip to main content

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

Quality of Life Research volume 21, pages 323–334 (2012)Cite this article

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.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

43,34 €

Price includes VAT (Finland)
Tax calculation will be finalised during checkout.

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

References

  1. Gold, M. R., Siegel, J. E., Russell, L. B., & Weinstein, M. C. (Eds.). (1996). Cost-effectiveness in health and medicine. New York: Oxford University Press.

    Google Scholar 

  2. Fryback, D. G., Dunham, N. C., Palta, M., Hanmer, J., Buechner, J., Cherepanov, D., et al. (2007). US norms for six generic health-related quality-of-life indexes from the national health measurement study. Medical Care, 45(12), 1162–1170.

    PubMed  Article  Google Scholar 

  3. Gold, M. R., Stevenson, D., & Fryback, D. G. (2002). HALYS and QALYS and DALYS, Oh My: Similarities and differences in summary measures of population health. Annual Review of Public Health, 23, 115–134.

    PubMed  Article  Google Scholar 

  4. Kaplan, R. M. (2005). Measuring quality of life for policy analysis: Past, present and the future. In W. R. Lenderking & D. A. Revicki (Eds.), Advancing health outcomes research methods and clinical applications. McClean, VA: International Society for Quality of Life Research.

    Google Scholar 

  5. Lipscomb, J., Drummond, M., Fryback, D., Gold, M., & Revicki, D. (2009). Retaining, and enhancing, the QALY. Value Health, 12(Suppl 1), S18–S26.

    PubMed  Article  Google Scholar 

  6. Weinstein, M. C., Torrance, G., & McGuire, A. (2009). QALYs: The basics. Value Health, 12(Suppl 1), S5–S9.

    PubMed  Article  Google Scholar 

  7. Brazier, J., Czoski-Murray, C., Roberts, J., Brown, M., Symonds, T., & Kelleher, C. (2008). Estimation of a preference-based index from a condition-specific measure: The King’s health questionnaire. Medical Decision Making, 28(1), 113–126.

    PubMed  Article  Google Scholar 

  8. Torrance, G. W. (1986). Measurement of health state utilities for economic appraisal. Journal of Health Economics, 5(1), 1–30.

    PubMed  Article  CAS  Google Scholar 

  9. Feeny, D., Furlong, W., Torrance, G. W., Goldsmith, C. H., Zhu, Z., DePauw, S., et al. (2002). Multiattribute and single-attribute utility functions for the health utilities index mark 3 system. Medical Care, 40(2), 113–128.

    PubMed  Article  Google Scholar 

  10. Feeny, D., Torrance, G. W., & Furlong, W. (1996). Health utilities index. In B. Spilker (Ed.), Quality of life and pharmacoeconomics in clinical trials. Philadelphia, PA: Lippincott-Raven Press.

    Google Scholar 

  11. The EuroQol Group. (1990). EuroQol–a new facility for the measurement of health-related quality of life. Health Policy, 16(3), 199–208.

    Article  Google Scholar 

  12. Brooks, R., Rabin, R., & de Charro, F. (2003). The measurement and valuation of health status using EQ-5D: A European perspective. Dordrecht, Netherlands: Kluwer Academic Publishers.

    Google Scholar 

  13. Andresen, E. M., Rothenberg, B. M., & Kaplan, R. M. (1998). Performance of a self-administered mailed version of the quality of well-being (QWB-SA) questionnaire among older adults. Medical Care, 36(9), 1349–1360.

    PubMed  Article  CAS  Google Scholar 

  14. Brown, G. C., Brown, M. M., Sharma, S., Stein, J. D., Roth, Z., Campanella, J. & Beauchamp, G. R. (2005). The burden of age-related macular degeneration: A value-based medicine analysis. Transactions of the American Ophthalmological Society, 103, 173–184; discussion 184–176.

    Google Scholar 

  15. Brown, M. M., Brown, G. C., Stein, J. D., Roth, Z., Campanella, J., & Beauchamp, G. R. (2005). Age-related macular degeneration: Economic burden and value-based medicine analysis. Canadian Journal of Ophthalmology, 40(3), 277–287.

    PubMed  Google Scholar 

  16. Espallargues, M., Czoski-Murray, C. J., Bansback, N. J., Carlton, J., Lewis, G. M., Hughes, L. A., et al. (2005). The impact of age-related macular degeneration on health status utility values. Investigative Ophthalmology and Visual Science, 46(11), 4016–4023.

    PubMed  Article  Google Scholar 

  17. National Institute for Health and Clinical Excellence. (2008). Guide to the methods of technology appraisal.

  18. Kaplan, R. M., Tally, S., Hays, R. D., Feeny, D., Ganiats, T. G., Palta, M. & Fryback, D. (2011). Responsiveness of five preference-based indexes in cataract and heart failure patients. Journal of Clinical Epidemiology, 64, 497–506.

    Google Scholar 

  19. Clarke, P., Gray, A., Legood, R., Briggs, A., & Holman, R. (2003). The impact of diabetes-related complications on healthcare costs: results from the United Kingdom prospective diabetes study (UKPDS study no. 65). Diabetic Medicine, 20(6), 442–450.

    PubMed  Article  CAS  Google Scholar 

  20. Brown, M. M., Brown, G. C., Sharma, S., & Shah, G. (1999). Utility values and diabetic retinopathy. American Journal of Ophthalmology, 128(3), 324–330.

    PubMed  Article  CAS  Google Scholar 

  21. Brown, G. C., Sharma, S., Brown, M. M., & Kistler, J. (2000). Utility values and age-related macular degeneration. Archives of Ophthalmology, 118(1), 47–51.

    PubMed  CAS  Google Scholar 

  22. Brown, M. M., Brown, G. C., Sharma, S., Landy, J., & Bakal, J. (2002). Quality of life with visual acuity loss from diabetic retinopathy and age-related macular degeneration. Archives of Ophthalmology, 120(4), 481–484.

    PubMed  Google Scholar 

  23. Brazier, J., Roberts, J., & Deverill, M. (2002). The estimation of a preference-based measure of health from the SF-36. Journal of Health Economics, 21(2), 271–292.

    PubMed  Article  Google Scholar 

  24. Revicki, D. A., Leidy, N. K., Brennan-Diemer, F., Sorensen, S., & Togias, A. (1998). Integrating patient preferences into health outcomes assessment: The multiattribute Asthma symptom utility index. Chest, 114(4), 998–1007.

    PubMed  Article  CAS  Google Scholar 

  25. Revicki, D. A., Leidy, N. K., Brennan-Diemer, F., Thompson, C., & Togias, A. (1998). Development and preliminary validation of the multiattribute Rhinitis symptom utility index. Quality of Life Research, 7(8), 693–702.

    PubMed  Article  CAS  Google Scholar 

  26. Young, T., Yang, Y., Brazier, J. E., Tsuchiya, A., & Coyne, K. (2009). The first stage of developing preference-based measures: constructing a health-state classification using Rasch analysis. Quality of Life Research, 18(2), 253–265.

    PubMed  Article  Google Scholar 

  27. Lenert, L. A., Sherbourne, C. D., Sugar, C., & Wells, K. B. (2000). Estimation of utilities for the effects of depression from the SF-12. Medical Care, 38(7), 763–770.

    PubMed  Article  CAS  Google Scholar 

  28. Lo, P. S., Tong, M. C., Revicki, D. A., Lee, C. C., Woo, J. K., Lam, H. C., et al. (2006). Rhinitis symptom utility index (RSUI) in Chinese subjects: a multiattribute patient-preference approach. Quality of Life Research, 15(5), 877–887.

    PubMed  Article  Google Scholar 

  29. Mangione, C. M., Lee, P. P., Gutierrez, P. R., Spritzer, K., Berry, S., & Hays, R. D. (2001). Development of the 25-item national eye institute visual function questionnaire. Archives of Ophthalmology, 119(7), 1050–1058.

    PubMed  CAS  Google Scholar 

  30. Matchar, D. B., Suner, I. J., Samsa, G. P., McCrory, D. C., Cline, K., & Lee, P. P. (2005). Measuring quality of life for patients with age-related macular degeneration. Rockville, MD: Technology Assessment Program, US Agency for Healthcare Research and Quality.

    Google Scholar 

  31. Mavranezouli, I., Brazier, J. E., Young, T. A. & Barkham, M. (2009). Using Rasch analysis to form plausible health states amenable to valuation: the development of CORE-6D from CORE-OM in order to elicit preferences for common mental health problems. Health Economics and Decision Science Discussion Paper Series No. 09/09: University of Sheffield.

  32. Mangione, C. M. (2000). NEI VFQ-25 scoring algorithm. Rockville, MD: National Eye Institute.

    Google Scholar 

  33. Mangione, C. M., Lee, P. P., Pitts, J., Gutierrez, P., Berry, S., Hays, R. D., et al. (1998). Psychometric properties of the national eye institute visual function questionnaire (NEI-VFQ). Archives of Ophthalmology, 116(11), 1496–1504.

    PubMed  CAS  Google Scholar 

  34. Revicki, D., Rentz, A., Harnam, N., Thomas, V. & Lanzetta, P. (2010). Reliability and validity of the national eye institute visual function questionnaire-25 in patients with age-related macular degeneration. Investigative Ophthalmology and Visual Science, 51(2), 712–717.

    Google Scholar 

  35. Submacular Surgery Trials Research Group. (2005). Health-related quality of life among patients with ocular histoplasmosis or idiopathic choroidal neovascularization at enrollment in a randomized trial of submacular surgery. SST report no. 5. Archives of Ophthalmology 123, 78–88.

    Google Scholar 

  36. Clemons, T. E., Chew, E. Y., Bressler, S. B., & McBee, W. (2003). National eye institute visual function questionnaire in the age-related eye disease study (AREDS): AREDS report no. 10. Archives of Ophthalmology, 121(2), 211–217.

    PubMed  Google Scholar 

  37. Lindblad, A. S., & Clemons, T. E. (2005). Responsiveness of the national eye institute visual function questionnaire to progression to advanced age-related macular degeneration, vision loss, and lens opacity: AREDS report no. 14. Archives of Ophthalmology, 123(9), 1207–1214.

    PubMed  Article  Google Scholar 

  38. Suner, I. J., Kokame, G. T., Yu, E., Ward, J., Dolan, C., & Bressler, N. M. (2009). Responsiveness of NEI VFQ-25 to changes in visual acuity in neovascular AMD: Validation studies from two phase 3 clinical trials. Investigative Ophthalmology and Visual Science, 50(8), 3629–3635.

    PubMed  Article  Google Scholar 

  39. Bond, T. G., & Fox, C. M. (2007). Applying the Rasch model: Fundamental measurement in the human sciences. London: Psychology Press.

    Google Scholar 

  40. Wright, B. D., & Masters, G. N. (1982). Rating scale analysis: Rasch measurement. Chicago: MESA Press.

    Google Scholar 

  41. Rasch, G. (1980). Probabilistic models for some intelligence and attainment tests. Chicago: University of Chicago Press.

    Google Scholar 

  42. Linacre, J. M. (1999). Investigating rating scale category utility. Journal of Outcome Measurement, 3(2), 103–122.

    PubMed  CAS  Google Scholar 

  43. Kubinger, K. D. (2005). Psychological test calibration using the Rasch model-some critical suggestions on traditional approaches. International Journal of Testing, 5(4), 377–394.

    Article  Google Scholar 

  44. RUMM 2020. (2004). Rasch unidimensional measurement models. Australia: RUMM Laboratory Pty Ltd.

  45. Kind, P., & Macran, S. (2005). Eliciting social preference weights for functional assessment of cancer therapy-lung health states. Pharmacoeconomics, 23(11), 1143–1153.

    PubMed  Article  Google Scholar 

  46. Allergan Inc. A study of the safety and efficacy of a new treatment for macular edema resulting from retinal vein occlusion. Baseline data on file at Allergan Inc.

  47. Allergan Inc. A study of safety and efficacy of a new treatment for diabetic Macular Edema. Baseline data on file at Allergan Inc.

  48. Schiffman, R. M., Walt, J. G., Jacobsen, G., Doyle, J. J., Lebovics, G., & Sumner, W. (2003). Utility assessment among patients with dry eye disease. Ophthalmology, 110(7), 1412–1419.

    PubMed  Article  Google Scholar 

  49. Globe, D. R., Wu, J., Azen, S. P., & Varma, R. (2004). The impact of visual impairment on self-reported visual functioning in Latinos: The Los Angeles Latino eye study. Ophthalmology, 111(6), 1141–1149.

    PubMed  Article  Google Scholar 

  50. Allergan Inc. Memantine in patients with chronic Glaucoma. Baseline data on file at Allergan Inc.

  51. Muir, K. W., Santiago-Turla, C., Stinnett, S. S., Herndon, L. W., Allingham, R. R., Challa, P., et al. (2006). Health literacy and adherence to glaucoma therapy. American Journal of Ophthalmology, 142(2), 223–226.

    PubMed  Article  Google Scholar 

Download references

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

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
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anne M. Rentz
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. John G. Walt
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Andrew Lloyd
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jeff Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Tracey A. Young
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Wen-Hung Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Neil M. Bressler
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Paul Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. John E. Brazier
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Ron D. Hays
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Dennis A. Revicki
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jonathan W. Kowalski.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

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

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11136-011-9938-z

Keywords

  • Vision-related functioning and well-being
  • Health-related quality of life
  • Central vision loss
  • Peripheral vision loss
  • Health preferences and utilities