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Quality of Life Research

, Volume 27, Issue 8, pp 2195–2206 | Cite as

A new algorithm to build bridges between two patient-reported health outcome instruments: the MOS SF-36® and the VR-12 Health Survey

  • Alfredo Selim
  • William Rogers
  • Shirley Qian
  • James A. Rothendler
  • Erin E. Kent
  • Lewis E. KazisEmail author
Article

Abstract

Purpose

To develop bridging algorithms to score the Veterans Rand-12 (VR-12) scales for comparability to those of the SF-36® for facilitating multi-cohort studies using data from the National Cancer Institute Surveillance, Epidemiology, and End Results Program (SEER) linked to Medicare Health Outcomes Survey (MHOS), and to provide a model for minimizing non-statistical error in pooled analyses stemming from changes to survey instruments over time.

Methods

Observational study of MHOS cohorts 1–12 (1998–2011). We modeled 2-year follow-up SF-36 scale scores from cohorts 1–6 based on baseline SF-36 scores, age, and gender, yielding 100 clusters using Classification and Regression Trees. Within each cluster, we averaged follow-up SF-36 scores. Using the same cluster specifications, expected follow-up SF-36 scores, based on cohorts 1–6, were computed for cohorts 7–8 (where the VR-12 was the follow-up survey). We created a new criterion validity measure, termed “extensibility,” calculated from the square root of the mean square difference between expected SF-36 scale averages and observed VR-12 item score from cohorts 7–8, weighted by cluster size. VR-12 items were rescored to minimize this quantity.

Results

Extensibility of rescored VR-12 items and scales was considerably improved from the “simple” scoring method for comparability to the SF-36 scales.

Conclusions

The algorithms are appropriate across a wide range of potential subsamples within the MHOS and provide robust application for future studies that span the SF-36 and VR-12 eras. It is possible that these surveys in a different setting outside the MHOS, especially in younger age groups, could produce somewhat different results.

Keywords

VR-12 SF-36 Extensibility 

Notes

Data availability

Several types of Medicare HOS data files are available for research purposes. Medicare HOS data files are available as public use files (PUFs), limited data sets (LDSs), and research identifiable files (RIFs). http://www.hosonline.org/en/datadissemination/research-data-files/

Author contributions

AS participated in the conception and design. WR participated in the analysis and interpretation of data. SQ performed the statistical analysis. JR participated in the conception and design and interpretation of data. EK participated in drafting and revising it critically for important intellectual content. LK participated in the study design and coordination and helped to draft the manuscript. All authors read and approved the final manuscript.

Funding

The study was supported by the Outcomes Research Branch/Healthcare Delivery Research Program Division of Cancer Control and Population Sciences, National Cancer Institute Contract # HHSN261201400530P. The views expressed represent those of the authors and not necessarily those of the National Cancer Institute, National Institutes of Health, and Boston University.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no competing interest.

Ethical approval

The Boston University Institutional Review Board (IRB) provided ethical review of this study to protect the rights and welfare of human subjects of research and to assure that human research is conducted according to applicable federal, state, and local laws and regulations, and the relevant policies of the Human Research Protection Program, Boston University.

Supplementary material

11136_2018_1850_MOESM1_ESM.doc (208 kb)
Supplementary material 1 (DOC 208 KB)

References

  1. 1.
    Scott, R. E., & Saeed, A. (2008). Global eHealth—measuring outcomes: Why, what, and how a report commissioned by the World Health Organization’s global observatory for eHealth. Retrieved September 10, 2017 from http://www.ehealth-connection.org/files/conf-materials/Global%20eHealth%20-%20Measuring%20Outcomes_0.pdf.
  2. 2.
    Jones, N. III, Jones, S. L., Miller, N. A. (2004). The medicare health outcomes survey program: Overview, context, and near-term prospects. Health and Qual Life Outcomes, 2, 33.CrossRefGoogle Scholar
  3. 3.
    Stewart, A. L., & Ware, J. (1992). Measuring functioning and well-being: The medical outcomes study approach. Durham: Duke University Press.CrossRefGoogle Scholar
  4. 4.
    Usman Iqbal, S., Rogers, W., Selim, A., Qian, S. X., Lee, A., Xinhua, X., Rothendler, J., Miller, D., & Kazis, L. (2007). The Veterans Rand 12 Item Health Survey (Vr-12): What it is and how it is used. Technical report. Retrieved October 28, 2015 from http://www.hosonline.org/globalassets/hos-online/publications/veterans_rand_12_item_health_survey_vr-12_2007.pdf.
  5. 5.
    Kazis, L. E., Selim, A. J., Rogers, W., Qian, S. X., & Brazier, J. (2012). Monitoring outcomes for the medicare advantage program: Methods and application of the VR-12 for evaluation of plans. The Journal of Ambulatory Care Management, 35, 263–276.CrossRefPubMedGoogle Scholar
  6. 6.
    Sprague, L. (2015). The star rating system and medicare advantage plans. Issue Brief National Health Policy Forum, 854, 1–10.Google Scholar
  7. 7.
    Warren, J. L., Klabunde, C. N., Schrag, D., Bach, P. B., & Riley, G. F. (2002). Overview of the SEER-medicare data: Content, research applications, and generalizability to the United States elderly population. Medical Care, 40(8 Suppl), 3–18.Google Scholar
  8. 8.
    Kent, E. E., Ambs, A., Mitchell, S. A., Clauser, S. B., Smith, A. W., & Hays, R. D. (2015). Health-related quality of life in older adult survivors of selected cancers: Data from the SEER-MMHOS linkage. Cancer, 121(5), 758–765.CrossRefPubMedGoogle Scholar
  9. 9.
    Quach, C., Sanoff, H. K., Williams, G. R., Lyons, J. C., & Reeve, B. B. (2015). Impact of colorectal cancer diagnosis and treatment on health-related quality of life among older Americans: A population-based, case-control study. Cancer, 121, 943–950.CrossRefPubMedGoogle Scholar
  10. 10.
    Stover, A. M., Mayer, D. K., Muss, H., Wheeler, S. B., Lyons, J. C., & Reeve, B. B. (2014). Quality of life changes during the pre- to postdiagnosis period and treatment-related recovery time in older women with breast cancer. Cancer, 120(12), 1881–1889.CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Ware, J. E., Bayliss, M. S., Rogers, W. H., Kosinski, M., & Tarlov, A. R. (1996). Differences in 4 year health outcomes for elderly and poor chronically Ill patients treated in HMO and fee-for-service systems. Results from the Medical Outcomes Study. JAMA, 276, 1039–1047.CrossRefPubMedGoogle Scholar
  12. 12.
    Kazis, L. E., Miller, D. R., Clark, J. A., Skinner, K. M., Lee, A., Ren, X. S., Spiro, A. 3rd, Rogers, W. H., & Ware, J. E. Jr. (2014). Improving the response choices on the Veterans SF-36 Health Survey role functioning scales: Results from the Veterans Health Study. The Journal of Ambulatory Care Management, 27(3), 263–280.CrossRefGoogle Scholar
  13. 13.
    Ware, J. E., Kosinski, M., Bayliss, M. S., McHorney, C. A., Rogers, W. H., & Raczek, A. (1995). Comparison of methods for scoring and statistical analysis of SF-36 health profiles and summary measures: Summary of results from the medical outcomes study. Medical Care, 33(suppl 4), AS264–AS279.PubMedGoogle Scholar
  14. 14.
    Coste, J., Quinquis, L., Audureau, E., & Pouchot, J. (2013). Non response, incomplete and inconsistent responses to self-administered health-related quality of life measures in the general population: Patterns, determinants and impact on the validity of estimates—a population-based study in France using the MOS SF-36. Health and Quality of Life Outcomes, 11, 44.CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Centers of Medicaid and Medicare Services. (2007). ‘Imputing the physical and mental summary scores (PCS and MCS) for the MOS SF-36 and the Veterans SF-36 Health Survey in the Presence of Missing Data. Retrieved October 28, 2015 from http://www.MHOSonline.org/surveys/MHOS/download/MHOS_Veterans_36_Imputation.pdf.
  16. 16.
    Ware, J. E. Jr., & Sherbourne, C. D. (1992). The MOS 36-item Short-Form Health Survey (SF-36): I. Conceptual framework and item selection. Medical Care, 30, 473–483.CrossRefPubMedGoogle Scholar
  17. 17.
    Ware, J. E., Snow, K. K., Kosinski, M., & Gandek, B. (1993). SF-36 Health Status Survey Manual and Interpretation Guide. Boston, MA: The Health Institute, New England Medical Center. Retrieved from The Medical Outcomes Trust.Google Scholar
  18. 18.
    Kanungo, T., Mount, D. M., Netanyahu, N. S., Piatko, C. D., Silverman, R., & Wu, A. (2002). An efficient k-means clustering algorithm: Analysis and implementation. Transactions on Pattern Analysis and Machine Intelligence, 24, 881–892.CrossRefGoogle Scholar
  19. 19.
    Crano, W. D., Brewer, M. B., & Lac, A. (2015). Principles and methods of social research (3rd ed.). New York, NY: Routledge.Google Scholar
  20. 20.
    Ware, J. E., Kosinski, M., & Keller, S. K. (1994). SF-36® physical and mental health summary scales: A user’s manual. Boston, MA: The Health Institute.Google Scholar
  21. 21.
    nl—Nonlinear least-squares estimation. Retrieved November 3, 2016 from http://www.stata.com/manuals13/rnl.pdf.
  22. 22.
    Ypma, T. J. (1195). Historical development of the Newton-Raphson method. SIAM Review, 37(4), 531–551.CrossRefGoogle Scholar
  23. 23.
    Rogers, W., Qian, S. X., & Kazis, L. E. (2004). Imputing the physical and mental summary scores (PCS and MCS) for the MOS SF-36 and the Veterans SF-36 Health Survey in the presence of Missing Data. Technical Report Prepared for the National Committee for Quality Assurance. Retrieved February 13, 2018 from http://www.hosonline.org/globalassets/hos-nline/publications/hos_veterans_36_imputation.pdf.
  24. 24.
    Spiro, A., Rogers, W., Qian, S. X., & Kazis, L. E. (2004). Imputing physical and mental summary scores (PCS and MCS) for the Veterans SF-12 Health Survey in the Context of Missing Data. Technical report prepared for the National Committee for Quality Assurance. Retrieved February 13, 2018 from http://www.hosonline.org/globalassets/hos-online/publications/hos_veterans_12_imputation.pdf.
  25. 25.
    Baker, F. B., & Kim, S.-H. (2004). Item response theory: Parameter estimation techniques (2nd ed.). New York: Marcel Dekker. ISBN 978-0-8247-5825-7.CrossRefGoogle Scholar

Copyright information

© This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2018

Authors and Affiliations

  • Alfredo Selim
    • 1
  • William Rogers
    • 1
  • Shirley Qian
    • 1
  • James A. Rothendler
    • 1
  • Erin E. Kent
    • 2
  • Lewis E. Kazis
    • 3
    Email author
  1. 1.Center for the Assessment of Pharmaceutical Practices (CAPP), Department of Health Law, Policy and ManagementBoston University School of Public HealthBostonUSA
  2. 2.Outcomes Research Branch/Healthcare Delivery Research Program, Division of Cancer Control and Population SciencesNational Cancer InstituteRockvilleUSA
  3. 3.Health Outcomes Unit, Department of Health Law, Policy and Management, Center for the Assessment of Pharmaceutical Practices (CAPP)Boston University School of Public HealthBostonUSA

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