Advertisement

Quality of Life Research

, Volume 27, Issue 7, pp 1835–1843 | Cite as

Evaluation of options for presenting health-states from PROMIS® item banks for valuation exercises

  • Janel Hanmer
  • David Cella
  • David Feeny
  • Baruch Fischhoff
  • Ron D. Hays
  • Rachel Hess
  • Paul A. Pilkonis
  • Dennis Revicki
  • Mark Roberts
  • Joel Tsevat
  • Lan Yu
Article

Abstract

Purpose

Health status descriptive systems based on item response theory (IRT), such as the Patient-Reported Outcomes Measurement Information System (PROMIS®), have item banks to measure domains of health. We developed a method to present such banks for health-state valuation.

Methods

We evaluated four different presentation approaches: a single item (1S), 2 items presented separately (2S), 2 items presented together (2T), or 5 items presented together (5T). We evaluated these four approaches in three PROMIS item banks (depression, physical function, and sleep disturbance). Adult community members valued health-state descriptions using the visual analog scale and standard gamble methods. We compared the approaches by the range of item bank theta scores captured, participants’ assessments of difficulty (1 = very easy to 7 = very hard), and exit interviews.

Results

Participants (n = 118) ranged in age from 18 to 71; 63% were female and 54% were white. The 1S approach captured the smallest range of theta scores. A monotonic relationship between theta score and mean standard gamble estimate was found with all approaches except 2S. Across all 3 item banks, mean difficulty assessments were 2.35 (1S), 2.69 (2T), 2.78 (5T), and 2.80 (2S). In exit interviews, participants generally found all four approaches similarly meaningful and realistic.

Conclusions

Creating health descriptions by presenting 2 items maximized the range of theta while minimizing difficulty and maintaining a monotonic relationship with utility estimates. We recommend this approach for valuation of IRT-based descriptive systems such as PROMIS.

Keywords

Health-state descriptions Preference-based scores Valuation of health-states Utilities 

Notes

Acknowledgements

Janel Hanmer was supported by the National Center for Advancing Translational Sciences of the National Institutes of Health under Award Number KL2TR001856. The project was supported by the National Institutes of Health through Grants Numbers UL1TR000005 and UL1TR001857, and a supplement to the PROMIS statistical center Grant 3U54AR057951-04S4. The funding agreements ensured the authors’ independence in designing the study, interpreting the data, writing, and publishing the report.

Compliance with ethical standards

Conflict of interest

The authors have no conflicts of interest to report.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. IRB approval for the project was obtained from the University of Pittsburgh (PRO14110193).

Supplementary material

11136_2018_1852_MOESM1_ESM.docx (3.4 mb)
Supplementary material 1 (DOCX 3527 KB)

References

  1. 1.
    McHorney, C. A. (1999). Health status assessment methods for adults: Past accomplishments and future challenges. Annual Review of Public Health, 20, 309–335.CrossRefPubMedGoogle Scholar
  2. 2.
    Torrance, G. W. (1986). Measurement of health state utilities for economic appraisal: a review. Journal of health economics, 5(1), 1–30.CrossRefPubMedGoogle Scholar
  3. 3.
    Drummond, M. F., Sculpher, M. J., Torrance, G. W., O’Brien, B. J., & Stoddart, G. L. (2005). Methods for the economic evaluation of health care programmes (3rd ed.). Oxford: Oxford University Press.Google Scholar
  4. 4.
    Neumann, P. J., Sanders, G. D., Russell, L. B., Siegel, J. E., & Ganiats, T. G. (Eds.). (2016). Cost-effectiveness in health and medicine (2nd ed.). Oxford: Oxford University Press.Google Scholar
  5. 5.
    Brooks, R., Rabin, R., & de Charro, F. (2003). The measurement and valuation of health status using EQ-5D: A European perspective. Dordrecht: Kluwer.CrossRefGoogle Scholar
  6. 6.
    Feeny, D., Furlong, W., Torrance, G. W., et al. (2002). Multiattribute and singleattribute utility functions for the health utilities index mark 3 system. Medical Care, 40, 113–128.CrossRefPubMedGoogle Scholar
  7. 7.
    Feeny, D., Torrance, G., & Furlong, W. (1996). Health utilities index. In B. Spilker (Ed.), Quality of life and pharmacoeconomics in clinical trials. Philadelphia: Lippincott-Raven Press.Google Scholar
  8. 8.
    Kaplan, R. M., & Anderson, J. P. (1988). A general health policy model: Update and applications. Health Services Research, 23, 203–234.PubMedPubMedCentralGoogle Scholar
  9. 9.
    Brazier, J. E., & Roberts, J. (2004). The estimation of a preference-based measure of health from the SF-12. Medical Care, 42, 851–859.CrossRefPubMedGoogle Scholar
  10. 10.
    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, 271–292.CrossRefPubMedGoogle Scholar
  11. 11.
    Cella, D., Yount, S., Rothrock, N., Gershon, R., Cook, K., Reeve, B., et al. (2007). The Patient-Reported Outcomes Measurement Information System (PROMIS): Progress of an NIH roadmap cooperative group during its first two years. Medical Care, 45(5), S3-11.Google Scholar
  12. 12.
    Cella, D., Riley, W., Reeve, B., Stone, A., Young, S., Rothrock, N., et al. (2010). Initial item banks and first wave testing of the Patient-Reported Outcomes Measurement Information System (PROMIS) network: 2005–2008. Journal of Clinical Epidemiology, 63(11), 1179–1194.CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Embretson, S. E., & Reise, S. P. (2000). Item response theory for psychologists. New York: Psychology Press.Google Scholar
  14. 14.
    Cook, K. F., Victorson, D. E., Cella, D., Schalet, B. D., & Miller, D. (2015). Creating meaningful cut-scores for Neuro-QOL measures of fatigue, physical functioning, and sleep disturbance using standard setting with patients and providers. Quality of Life Research, 24(3), 575–589.CrossRefPubMedGoogle Scholar
  15. 15.
    Thissen, D., Liu, Y., Magnus, B., Quinn, H., Gipson, D. S., Dampier, C., … Gross, H. E. (2016). Estimating minimally important difference (MID) in PROMIS pediatric measures using the scale-judgment method. Quality of Life Research, 25(1), 13–23.CrossRefPubMedGoogle Scholar
  16. 16.
    Pilkonis, P. A., Choi, S. W., Reise, S. P., Stover, A. M., Riley, W. T., & Cella, D. (2011). Item banks for measuring emotional distress from the Patient-Reported Outcomes Measurement Information System (PROMIS®): Depression, anxiety, and anger. Assessment, 18(3), 263–283.CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Rose, M., Bjorner, J. B., Gandek, B., Bruce, B., Fries, J. F., & Ware, J. E. (2014). The PROMIS Physical Function item bank was calibrated to a standardized metric and shown to improve measurement efficiency. Journal of Clinical Epidemiology, 67(5), 516–526.CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Buysse, D. J., Yu, L., Moul, D. E., Germain, A., Stover, A., Dodds, N. E., … Pilkonis, P. A. (2010). Development and validation of patient-reported outcome measures for sleep disturbance and sleep-related impairments. Sleep, 33(6), 781–792.CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    McNaughton, C. D., Cavanaugh, K. L., Kripalani, S., Rothman, R. L., & Wallston, K. A. (2015). Validation of a short, 3-item version of the Subjective Numeracy Scale. Medical Decision Making, 35(8), 932–936.CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Ironson, G., Solomon, G. F., Balbin, E. G., O’Cleirigh, C., George, A., Kumar, M., … Woods, T. E. (2002). The Ironson-Woods Spirituality/Religiousness Index is associated with long survival, health behaviors, less distress, and low cortisol in people with HIV/AIDS. Annals of Behavioral Medicine, 24(1), 34–48.CrossRefPubMedGoogle Scholar
  21. 21.
    PROMIS Depression Scoring Manual. (2015). Accessed August, 2017, from https://www.assessmentcenter.net/documents/PROMIS%20Depression%20Scoring%20Manual.pdf.
  22. 22.
    PROMIS Physical Function Scoring Manual. (2015). Accessed August, 2017, from https://www.assessmentcenter.net/documents/PROMIS%20Physical%20Function%20Scoring%20Manual.pdf.
  23. 23.
    PROMIS Sleep Disturbance Scoring Manual. (2015). Accessed August, 2017, from http://www.healthmeasures.net/images/promis/manuals/PROMIS_Sleep_Disturbance_Scoring_Manual.pdf.
  24. 24.
    Liu, H., Cella, D., Gershon, R., Shen, J., Morales, L. S., Riley, W., & Hays, R. D. (2010). Representativeness of the Patient-Reported Outcomes Measurement Information System internet panel. Journal of Clinical Epidemiology, 63(11), 1169–1178.CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Dobrez, D., Cella, D., Pickard, A. S., Lai, J. S., & Nickolov, A. (2007). Estimation of patient preference-based utility weights from the functional assessment of cancer therapy—General. Value in Health, 10(4), 266–272.CrossRefPubMedGoogle Scholar
  26. 26.
    Hanmer, J., Feeny, D., Fischhoff, B., Hays, R. D., Hess, R., Pilkonis, P. A., … Yu, L. (2015). The PROMIS of QALYs. Health and Quality of Life Outcomes, 13(1), 122.CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Janel Hanmer
    • 1
  • David Cella
    • 2
  • David Feeny
    • 3
    • 4
  • Baruch Fischhoff
    • 5
  • Ron D. Hays
    • 6
  • Rachel Hess
    • 7
  • Paul A. Pilkonis
    • 8
  • Dennis Revicki
    • 9
  • Mark Roberts
    • 10
    • 11
  • Joel Tsevat
    • 12
  • Lan Yu
    • 10
  1. 1.Department of General Internal MedicineUniversity of Pittsburgh Medical CenterPittsburghUSA
  2. 2.Department of Medical Social SciencesNorthwestern University Feinberg School of MedicineChicagoUSA
  3. 3.Department of EconomicsMcMaster UniversityHamiltonCanada
  4. 4.Health Utilities IncorporatedDundasCanada
  5. 5.Department of Engineering and Public Policy and Institute for Politics and StrategyCarnegie Mellon UniversityPittsburghUSA
  6. 6.Division of General Internal Medicine & Health Services ResearchUCLALos AngelesUSA
  7. 7.Division of Health System Innovation and ResearchUniversity of Utah Schools of the Health SciencesSalt Lake CityUSA
  8. 8.Department of PsychiatryUniversity of Pittsburgh Medical CenterPittsburghUSA
  9. 9.Outcomes Research, EvideraBethesdaUSA
  10. 10.Department of General Internal MedicineUniversity of Pittsburgh Medical CenterPittsburghUSA
  11. 11.Department of Health Policy and ManagementUniversity of PittsburghPittsburghUSA
  12. 12.Division of General Internal MedicineUniversity of Cincinnati College of Medicine and Cincinnati VA Medical CenterCincinnatiUSA

Personalised recommendations