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Do the unlabeled response categories of the Minnesota Living with Heart Failure Questionnaire satisfy the monotonicity assumption of simple-summated scoring?

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Abstract

Purpose

Half of the 21-item Minnesota Living with Heart Failure Questionnaire (MLHFQ) response categories are labeled (0 = No, 1 = Very little, 5 = Very much) and half are not (2, 3, and 4). We hypothesized that the unlabeled response options would not be more likely to be chosen at some place along the scale continuum than other response options and, therefore, not satisfy the monotonicity assumption of simple-summated scoring.

Methods

We performed exploratory and confirmatory factor analyses of the MLHFQ items in a sample of 1437 adults in the Better Effectiveness After Transition—Heart Failure study. We evaluated the unlabeled response options using item characteristic curves from item response theory—graded response models for MLHFQ physical and emotional health scales. Then, we examined the impact of collapsing response options on correlations of scale scores with other variables.

Results

The sample was 46% female; 71% aged 65 or older; 11% Hispanic, 22% Black, 54% White, and 12% other. The unlabeled response options were rarely chosen. The standard approach to scoring and scores obtained by collapsing adjacent response categories yielded similar associations with other variables, indicating that the existing response options are problematic.

Conclusions

The unlabeled MLHFQ response options do not meet the assumptions of simple-summated scoring. Further assessment of the performance of the unlabeled response options and evaluation of alternative scoring approaches is recommended. Adding labels for response options in future administrations of the MLHFQ should be considered.

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References

  1. Garin, O., Herdman, M., Vilagut, G., Ferrer, M., Ribera, A., Rajmil, L., et al. (2013). Assessing health-related quality of life in patients with heart failure: A systematic, standardized comparison of available measures. Heart Failure Reviews,19(3), 359–367.

    Article  Google Scholar 

  2. Rector, T. S., & Cohn, J. N. (1992). Assessment of patient outcome with the Minnesota living with heart failure questionnaire: Reliability and validity during a randomized, double-blind, placebo-controlled trial of pimobendan. American Heart Journal,124(4), 1017–1025.

    Article  CAS  PubMed  Google Scholar 

  3. Garin, O., Ferrer, M., Pont, À., Rué, M., Kotzeva, A., Wiklund, I., et al. (2008). Disease-specific health-related quality of life questionnaires for heart failure: A systematic review with meta-analyses. Quality of Life Research,18(1), 71–85.

    Article  PubMed  Google Scholar 

  4. U.S Food & Drug Administration. (2016). Medical device development tool (MDDT) Qualified Tools. Retrieved Jine 14, 2019, from https://www.fda.gov/medical-devices/science-and-research-medical-devices/medical-device-development-tools-mddt#Qualified_Tools.

  5. Pietri, G., Ganse, E. V., Ferrier, M., Garin, O., & Wiklund, I. (2004). Minnesota living with heart failure questionnaire. Lyon, France: MAPI Research Institute.

    Google Scholar 

  6. Moors, G., Kieruj, N. D., & Vermunt, J. K. (2014). The effect of labeling and numbering of response scales on the likelihood of response bias. Sociological Methodology,44(1), 369–399.

    Article  Google Scholar 

  7. Bilbao, A., Escobar, A., Garcia-Perez, L., et al. (2016). The Minnesota living with heart failure questionnaire: Comparison of different factor structures. Health Qual Life Outcomes,14, 23.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Munyombwe, T., Höfer, S., Fitzsimons, D., Thompson, D. R., Lane, D., Smith, K., et al. (2014). An evaluation of the Minnesota living with heart failure questionnaire using Rasch analysis. Quality of Life Research,23(6), 1753–1765.

    Article  PubMed  Google Scholar 

  9. Preston, K., Reise, S., Cai, L., & Hays, R. D. (2011). Using the nominal response model to evaluate response category discrimination in the PROMIS emotional distress item pools. Educational and Psychological Measurement,71(3), 523–550.

    Article  Google Scholar 

  10. Fayers, P. M., & Machin, D. (2016). Quality of life: The assessment, analysis, and reporting of patient-reported outcomes. Chichester, UK: Wiley.

    Google Scholar 

  11. Kaplan, R. M., & Saccuzzo, D. P. (2012). Psychological testing: Principles, applications, and issues (8th ed.). Belmont, CA: Wadsworth Publishing.

    Google Scholar 

  12. Hays, R. D., Spritzer, K. L., Thompson, W. W., & Cella, D. (2015). US general population estimate for “excellent”, to “poor” self-rated health item. Journal of General Internal Medicine,30(10), 1511–1516.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Black, J. T., Romano, P. S., Sadeghi, B., Auerbach, A. D., Ganiats, T. G., Greenfield, S., et al. (2014). A remote monitoring and telephone nurse coaching intervention to reduce readmissions among patients with heart failure: Study protocol for the Better Effectiveness After Transition-Heart Failure (BEAT-HF) randomized controlled trial. Trials,151, 124.

    Article  Google Scholar 

  14. Ong, M. K., Romano, P. S., Edgington, S., Aronow, H. U., Auerbach, A. D., Black, J. T., et al. (2016). Effectiveness of remote patient monitoring after discharge of hospitalized patients with heart failure. JAMA Internal Medicine,176(3), 310.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika,16(3), 297–334.

    Article  Google Scholar 

  16. Spritzer, K.L., Hays, R.D.. A SAS macro (ordalpha) to computer ordinal coefficient alpha. Retrieved June 14, 2019, from https://labs.dgsom.ucla.edu/hays/pages/programs_utilities.

  17. Samejima, F. (1997). Graded response model. In W. J. van der Linden & R. K. Hambleton (Eds.) Handbook of modern item response theory. New York, NY: Springer.

    Google Scholar 

  18. Watanabe, S. (2013). A widely applicable Bayesian information criterion. Journal of Machine Learning Research,14, 867–897.

    Google Scholar 

  19. Bürkner, P. (2019). Bayesian item response modelling in R with brms and Stan. arXiv preprint arXiv:1905.09501.

  20. Gelman, A., & Hill, J. (2006). Data analysis using regression and multilevel/hierarchical models. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  21. Dolgin, M., & New York Heart Association Criteria Committee. (1973). Nomenclature and criteria for diagnosis of diseases of the heart and great vessels. Boston: Little, Brown.

    Google Scholar 

  22. Sheikh, J. I., & Yesavage, J. A. (1986). Geriatric Depression Scale (GDS): Recent evidence and development of a shorter version. Clinical Gerontology,5(1–2), 165–173.

    Google Scholar 

  23. Michelson, H., Bolund, C., & Brandberg, Y. (2000). Multiple chronic health problems are negatively associated with health-related quality of life (HRQoL) irrespective of age. Quality of Life Research,9(10), 1093–1104.

    Article  CAS  PubMed  Google Scholar 

  24. Fortin, M., Bravo, G., Hudon, C., Lapointe, L., Almirall, J., Dubois, M. F., et al. (2006). Relationship between multimorbidity and health-related quality of life in patients in primary care. Quality of Life Research,15(1), 83–91.

    Article  PubMed  Google Scholar 

  25. Moore, B. J., White, S., Washington, R., Coenen, N., & Elixhauser, A. (2017). Identifying increased risk of readmission and in-hospital mortality using hospital administrative data: The AHRQ Elixhauser comorbidity index. Medical Care,55(7), 698–705.

    Article  PubMed  Google Scholar 

  26. Walraven, C. V., Austin, P. C., Jennings, A., Quan, H., & Forster, A. J. (2009). A modification of the elixhauser comorbidity measures into a point system for hospital death using administrative data. Medical Care,47(6), 626–633.

    Article  PubMed  Google Scholar 

  27. Bürkner, P. (2017). brms: An R package for Bayesian multilevel models using Stan. Journal of Statistical Software,80(1), 1–28.

    Article  Google Scholar 

  28. Stan Development Team. (2018). Stan modeling language user’s guide and reference manual. Version 2.18.0. Retrieved from https://mc-stan.org.

  29. StataCorp., (2015). Stata statistical software: release 14. College Station, TX: StataCorp LP.

    Google Scholar 

  30. Food & Drug Administration. (2016). Medical device development tool (MDDT) qualification decision summary for Minnesota living with heart failure questionnaire (MLHFQ). Retrieved June 14, 2019, from https://www.fda.gov/media/112157/download.

  31. Hays, R. D., Morales, L. S., & Reise, S. P. (2000). Item response theory and health outcomes measurement in the 21st century. Medical Care,38(9), II28–II42.

    CAS  Google Scholar 

  32. Hak, T., Willems, D., Wal, G. V., & Visser, F. (2004). A qualitative validation of the Minnesota living with heart failure questionnaire. Quality of Life Research,13(2), 417–426.

    Article  PubMed  Google Scholar 

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Funding

This study was funded in part by the Agency for Healthcare Research and Quality (R01 HS019311), the National Heart Lung and Blood Institute (RC2 HL101811), the National Institute on Aging (P30 AG021684), the Robert Wood Johnson Foundation (66336), and the Sierra Health Foundation, and the participating institutions.

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Correspondence to Ron D. Hays.

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The authors declare that they have no conflict of interest.

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UCLA Institutional Review Board approved the study.

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All study participants provided informed consent.

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Uy, V., Hays, R.D., Xu, J.J. et al. Do the unlabeled response categories of the Minnesota Living with Heart Failure Questionnaire satisfy the monotonicity assumption of simple-summated scoring?. Qual Life Res 29, 1349–1360 (2020). https://doi.org/10.1007/s11136-020-02422-8

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  • DOI: https://doi.org/10.1007/s11136-020-02422-8

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