Advertisement

Quality of Life Research

, Volume 28, Issue 1, pp 131–139 | Cite as

Mapping the Alzheimer’s Disease Cooperative Study-Activities of Daily Living Inventory to the Health Utility Index Mark III

  • Yin Bun CheungEmail author
  • Hui Xing Tan
  • Vivian Wei Wang
  • Nagaendran Kandiah
  • Nan Luo
  • Gerald C. H. Koh
  • Hwee Lin Wee
Article

Abstract

Purpose

To map the Alzheimer’s Disease Cooperative Study—Activities of Daily Living Inventory (ADCS-ADL) to the Health Utility Index Mark III (HUI3) in people living with dementia (PWD) and to compare the performance of five methods for mapping.

Methods

A cross-sectional study of 346 dyads of community-dwelling PWD and family caregiver was carried out in Singapore. ADCS-ADL and HUI3 were rated by the family caregivers. Disease severity ratings and Mini Mental State Examination (MMSE) results were retrieved from medical records. A recently proposed mapping method called the Mean Rank Method (MRM) was described and applied, and the results were compared with regression-based mapping, including ordinary least squares, censored least absolute deviation (CLAD), Tobit and response mapping.

Results

The MRM produced a mapped utility distribution that closely resembled the observed utility distribution. The standard deviations (SDs) of the observed and MRM-mapped utility were both 0.340, whereas the SDs of the other mapped utilities ranged from 0.243 (response mapping) to 0.283 (CLAD). Regressing the MRM- and CLAD-mapped and observed utility values upon disease severity and MMSE gave similar regression lines (each P > 0.05). Regressing the other mapped utility values upon the covariates under- (over-) estimated the utility of good (poor) clinical states. However, regression-based mapping methods gave a better fit at the individual level, as measured by root mean square error, mean absolute error and R2. K fold cross-validation gave similar results.

Conclusions

The MRM is accurate at the group level. The regression-based mapping methods are more accurate for making individual-level prediction. In addition, CLAD also performed reasonably well at the group level.

Keywords

Activities of daily living Dementia Health utility Health Utility Index Mark III Mapping 

Notes

Author contributions

VWW, NK and HLW designed and conducted the cross-sectional study of dementia patients and caregivers. YBC and HLW conceived this specific aim for mapping ADL inventory to health utilities. YBC, HLW, NL and GCHK contributed to the development of the analysis strategy. YBC and HXT implemented the statistical analysis. YBC wrote the first draft of the article. All the authors critically reviewed the article and agreed with the submission.

Compliance with ethical standards

Conflict of interest

All authors declare that they have no conflict of interest.

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.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Supplementary material

11136_2018_1991_MOESM1_ESM.docx (12 kb)
Supplementary material 1 (DOCX 12 KB)
11136_2018_1991_MOESM2_ESM.docx (22 kb)
Supplementary material 2 (DOCX 21 KB)
11136_2018_1991_MOESM3_ESM.xls (50 kb)
Supplementary material 3 (XLS 49 KB)

References

  1. 1.
    Drummond, M. F., Sculpher, M. J., Claxton, K., et al. (2015). Methods for the economic evaluation of health care programmes. Oxford: Oxford University Press.Google Scholar
  2. 2.
    Whitehead, S. J., & Ali, S. (2010). Health outcomes in economic evaluation: The QALY and utilities. British Medical Bulletin, 96, 5–21.CrossRefGoogle Scholar
  3. 3.
    Herdman, M., Gudex, C., Lloyd, A., et al. (2011). Development and preliminary testing of the new five-level version of EQ-5D (EQ-5D–5L). Quality of Life Research, 20(10), 1727–1736.CrossRefGoogle Scholar
  4. 4.
    Horsman, J., Furlong, W., Feeny, D., et al. (2003). The Health Utilities Index (HUI): Concepts, measurement properties and applications. Health and Quality of Life Outcomes, 1, 54.CrossRefGoogle Scholar
  5. 5.
    Brazier, J., Usherwood, T., Harper, R., & Thomas, K. (1998). Deriving a preference-based single index from the UK SF-36 Health Survey. Journal of Clinical Epidemiology, 51(11), 1115–1128.CrossRefGoogle Scholar
  6. 6.
    Longworth, L., Yang, Y., Young, T., et al. (2014). Use of generic and condition-specific measures of health-related quality of life in NICE decision-making: A systematic review, statistical modelling and survey. Health Technology Assessment.  https://doi.org/10.3310/hta18090.Google Scholar
  7. 7.
    Fayers, P. M., & Hays, R. D. (2014). Should linking replace regression when mapping from profile-based measures to preference-based measures? Value in Health, 17(2), 261–265.CrossRefGoogle Scholar
  8. 8.
    Fayers, P. M., & Machin, D. (2016). Quality of life: The assessment, analysis and reporting of patient-reported outcomes (3rd ed.). Oxford: Wiley.Google Scholar
  9. 9.
    Whately-Smith, C., Watkins, C., Mann, H., Fletcher, C., & Ducournau, P. (2014). Utility values in health technology assessments: A statistician’s perspective. Pharmaceutical Statistics, 13(3), 184–195.CrossRefGoogle Scholar
  10. 10.
    Crott, R. (2014). Mapping algorithms from QLQ-C30 to EQ-5D utilities: No firm ground to stand on yet. Expert Review of Pharmacoeconomics and Outcomes Research, 14(4), 569–576.CrossRefGoogle Scholar
  11. 11.
    National Institute for Health and Care Excellence. (2013). Guide to the methods of technology appraisal. London: National Institute for Health and Care Excellence.Google Scholar
  12. 12.
    Longworth, L., & Rowen, D. (2013). Mapping to obtain EQ-5D utility values for use in NICE health technology assessments. Value in Health, 16(1), 202–210.CrossRefGoogle Scholar
  13. 13.
    Brazier, J., Yang, Y., Tsuchiya, A., & Rowen, D. L. (2010). A review of studies mapping (or cross walking) non-preference based measures of health to generic preference-based measures. European Journal of Health Economics, 11, 215–225.CrossRefGoogle Scholar
  14. 14.
    Dakin, H. (2013). Review of studies mapping from quality of life or clinical measures to EQ-5D: An online database. Health and Quality of Life Outcomes, 11, 151.CrossRefGoogle Scholar
  15. 15.
    Singapore Ministry of Health. (2016). ElderShield fast facts. Singapore: Ministry of Health. http://www.eldershield.sg.
  16. 16.
    Galasko, D., Bennett, D., Sano, M., et al. (1997). An inventory to assess activities of daily living for clinical trials in Alzheimer’s disease. The Alzheimer’s Disease Cooperative Study. Alzheimer Disease and Associated Disorders, 11(Suppl. 2), S33–S39.CrossRefGoogle Scholar
  17. 17.
    Robert, P., Ferris, S., Gauthier, S., et al. (2010). Review of Alzheimer’s disease scales: Is there a need for a new multi-domain scale for therapy evaluation in medical practice? Alzheimer’s Research and Therapy, 26(2), 24.CrossRefGoogle Scholar
  18. 18.
    Wee, H. L., Yeo, K. K., Chong, K. J., Khoo, E. Y. H., & Cheung, Y. B. (2018). Mean rank, equipercentile and regression mapping of World Health Organization Quality of Life Brief (WHOQOL-BREF) to EuroQoL 5 Dimensions 5 Levels (EQ-5D–5L) utilities. Medical Decision Making, 38(3), 319–333.CrossRefGoogle Scholar
  19. 19.
    Cheung, Y. B., Thumboo, J., Gao, F., et al. (2009). Mapping the English and Chinese versions of the Functional Assessment of Cancer Therapy-General to the EQ-5D utility index. Value in Health, 12(2), 371–376.CrossRefGoogle Scholar
  20. 20.
    Cheung, Y. B., Luo, N., Ng, R., & Lee, C. F. (2014). Mapping the Functional Assessment of Cancer Therapy-Breast (FACT-B) to the 5-level EuroQoL Group’s 5-dimension questionnaire (EQ-5D–5L) utility index in a Multi-ethnic Asian Population. Health and Quality of Life Outcomes, 12, 180.CrossRefGoogle Scholar
  21. 21.
    Gray, A. M., Rivero-Arias, O., & Clarke, P. M. (2006). Estimating the association between SF-12 responses and EQ-5D utility values by response mapping. Medical Decision Making, 26(1), 18–29.CrossRefGoogle Scholar
  22. 22.
    Huang, I. C., Frangakis, C., Atkinson, M. J., et al. (2008). Addressing ceiling effects in health status measures: A comparison of techniques applied to measures for people with HIV disease. Health Services Research, 43, 327–339.CrossRefGoogle Scholar
  23. 23.
    Sullivan, P. W., & Ghushchyan, V. (2006). Mapping the EQ-5D index from the SF-12: US general population preferences in a nationally representative sample. Medical Decision Making, 26(4), 401–409.CrossRefGoogle Scholar
  24. 24.
    Dorans, N. J. (2007). Linking scores from multiple health outcome instruments. Quality of Life Research, 16(Suppl. 1), 85–94.CrossRefGoogle Scholar
  25. 25.
    Holland, P. W., & Thayer, D. T. (1989). The kernel method of equating score distributions. Washington, DC: Educational Testing Service.CrossRefGoogle Scholar
  26. 26.
    Chan, A., Ostbye, T., Malhotra, R., & Hu, A. J. (2012). The survey on informal caregiving: The summary report for MCYS. Retrieved June 1, 2013, from https://app.msf.gov.sg/Portals/0/Informal%20Caregiver%20Survey%20Summary%20Report%20(upload).pdf.
  27. 27.
    Contador, I., Fernandez-Calvo, B., Palenzuela, D. L., Migueis, S., & Ramos, F. (2012). Prediction of burden in family caregivers of patients with dementia: A perspective of optimism based on generalized expectancies of control. Aging and Mental Health, 16(6), 675–682.CrossRefGoogle Scholar
  28. 28.
    Haro, J. M., Kahle-Wrobleski, K., Bruno, G., et al. (2014). Analysis of burden in caregivers of people with Alzheimer’s disease using self-report and supervision hours. Journal of Nutrition, Health and Aging, 18(7), 677–684.CrossRefGoogle Scholar
  29. 29.
    Reed, C., Belger, M., Dell’agnello, G., et al. (2014). Caregiver burden in Alzheimer’s disease: Differential associations in adult–child and spousal caregivers in the GERAS observational study. Dementia and Geriatric Cognitive Disorders Extra, 4(1), 51–64.CrossRefGoogle Scholar
  30. 30.
    Bhattacharya, S., Vogel, A., Hansen, M. L., et al. (2010). Generic and disease-specific measures of quality of life in patients with mild Alzheimer’s disease. Dementia and Geriatric Cognitive Disorders Extra, 30, 327–333.CrossRefGoogle Scholar
  31. 31.
    Coucill, W., Bryan, S., Bentham, P., Buckley, A., & Laight, A. (2001). EQ-5D in patients with dementia: An investigation of inter-rater agreement. Medical Care, 39(8), 760–771.CrossRefGoogle Scholar
  32. 32.
    Karlawish, J. H., Zbrozek, A., Kinosian, B., Gregory, A., Ferguson, A., & Glick, H. A. (2008). Preference-based quality of life in patients with Alzheimer’s disease. Alzheimer’s and Dementia, 4(3), 193–202.CrossRefGoogle Scholar
  33. 33.
    Wang, W. (2016). Economic and health related quality of life outcomes among community-dwelling dementia patients in Singapore. PhD Dissertation, National University of Singapore, Singapore.Google Scholar
  34. 34.
    Feeny, D., Furlong, W., Torrance, G. W., et al. (2002). Multiattribute and single-attribute utility functions for the Health Utilities Index Mark 3 system. Medical Care, 40(2), 113–128.CrossRefGoogle Scholar
  35. 35.
    Folstein, M. F., Folstein, S. E., & McHugh, P. R. (1975). “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research 1975, 12(3), 189–198.CrossRefGoogle Scholar
  36. 36.
    Feng, L., Chong, M. S., Lim, W. S., & Ng, T. P. The Modified Mini-Mental State Examination test: Normative data for Singapore Chinese older adults and its performance in detecting early cognitive impairment. Singapore Medical Journal, 53(7), 458–462.Google Scholar
  37. 37.
    Morris, J. C. (1993). The Clinical Dementia Rating (CDR): Current version and scoring rules. Neurology, 43(11), 2412–2414.CrossRefGoogle Scholar
  38. 38.
    Royston, P., & Altman, D. G. (1994). Regression using fractional polynomials of continuous covariates: Parsimonious parametric modeling (with discussion). Applied Statistics, 43, 429–467.CrossRefGoogle Scholar
  39. 39.
    James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning, with applications in R. New York: Springer.CrossRefGoogle Scholar
  40. 40.
    Greene, W. H. (2012). Econometric analysis (7th ed.). Upper Saddle River, NJ: Prentice Hall.Google Scholar
  41. 41.
    Lee, C. F., Ng, R., Luo, N., & Cheung, Y. B. (2018) Development of conversion functions mapping the FACT-B total score to the EQ-5D–5L utility value by three linking methods and comparison with the ordinary least square method. Applied Health Economics and Health Policy.  https://doi.org/10.1007/s40258-018-0404-8 (E-pub ahead of print).Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Center for Quantitative MedicineDuke-NUS Medical SchoolSingaporeSingapore
  2. 2.Center for Child Health ResearchUniversity of Tampere and Tampere University HospitalTampereFinland
  3. 3.Saw Swee Hock School of Public HealthNational University of SingaporeSingaporeSingapore
  4. 4.Department of Hospital ManagementFudan UniversityShanghaiChina
  5. 5.Department of NeurologyNational Neuroscience InstituteSingaporeSingapore
  6. 6.Department of PharmacyNational University of SingaporeSingaporeSingapore

Personalised recommendations