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Underestimation of Uncertainties in Health Utilities Derived from Mapping Algorithms Involving Health-Related Quality-of-Life Measures

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  • Kelvin K. W. Chan
  • Andrew R. Willan
  • Michael Gupta
  • Eleanor Pullenayegum

Abstract

Objectives. Mapping algorithms are being developed in increasing numbers to derive health utilities (HUs) from health-related quality-of-life (HRQOL) data. However, the variances of the mapping-derived HUs are observed to be smaller than those of the actual HUs. Methods. Two reasons are proposed: 1) the presence of important unmeasured predictors leading to a high degree of unexplained variance and 2) ignoring that the regression coefficients are random variables themselves. We derive 3 variance estimators of HUs to account for these causes: 1) R 2 -adjusted estimator, 2) parametric estimator, and 3) nonparametric estimator. We tested these estimators using a simulated dataset and a real dataset involving the EQ-5D-3L and University of Washington Quality of Life questionnaire for patients with head and neck cancers. Results. The R 2 -adjusted estimator can be used in ordinary least squares (OLS)–based mapping algorithms and requires only the R 2 from the derivation study. The parametric estimator can be used in OLS-based mapping algorithms and requires the mean square error (MSE) and design matrix from the derivation study. The nonparametric estimator can be used in any mapping algorithm and requires leave-one-out cross-validation MSE from the derivation study. In the simulated dataset, all 3 estimators are within 1% of the variance of the actual HUs. In the real dataset, the unadjusted variance was 45% less than the actual variance, while all 3 estimators are within 10% of the actual variance. Conclusions. When conducting cost-utility analyses (CUA) based on mapping algorithms, the variances of derived HUs should be properly adjusted using one of the proposed methods so that the results of the CUAs will correctly characterize uncertainty.

Suggested Citation

  • Kelvin K. W. Chan & Andrew R. Willan & Michael Gupta & Eleanor Pullenayegum, 2014. "Underestimation of Uncertainties in Health Utilities Derived from Mapping Algorithms Involving Health-Related Quality-of-Life Measures," Medical Decision Making, , vol. 34(7), pages 863-872, October.
  • Handle: RePEc:sae:medema:v:34:y:2014:i:7:p:863-872
    DOI: 10.1177/0272989X13517750
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    References listed on IDEAS

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    1. Duncan Mortimer & Leonie Segal, 2008. "Comparing the Incomparable? A Systematic Review of Competing Techniques for Converting Descriptive Measures of Health Status into QALY-Weights," Medical Decision Making, , vol. 28(1), pages 66-89, January.
    2. John Brazier & Yaling Yang & Aki Tsuchiya & Donna Rowen, 2010. "A review of studies mapping (or cross walking) non-preference based measures of health to generic preference-based measures," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 11(2), pages 215-225, April.
    3. Andrew R. Willan & Bernie J. O'Brien, 2001. "Cost prediction models for the comparison of two groups," Health Economics, John Wiley & Sons, Ltd., vol. 10(4), pages 363-366, June.
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    1. Krishnakumar Thankappan & Tejal Patel & Krishna Kollamparambil Ajithkumar & Deepak Balasubramanian & Manu Raj & Sujha Subramanian & Subramania Iyer, 2022. "Mapping of head and neck cancer patient concerns inventory scores on to Euroqol-Five Dimensions-Five Levels (EQ-5D-5L) health utility scores," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 23(2), pages 225-235, March.
    2. Zafar Zafari & Mohsen Sadatsafavi & Carlo A Marra & Wenjia Chen & J Mark FitzGerald, 2016. "Cost-Effectiveness of Bronchial Thermoplasty, Omalizumab, and Standard Therapy for Moderate-to-Severe Allergic Asthma," PLOS ONE, Public Library of Science, vol. 11(1), pages 1-15, January.

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