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An assessment of the external validity of mapping QLQ-C30 to EQ-5D preferences

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Abstract

Background

Although cancer-specific Health-related Quality-of-Life measures are commonly included in randomized clinical trials or other prospective non-randomized clinical studies, it is rare that preference-based instruments are used, which allow the calculation of a Utility weight suitable for estimating Quality-adjusted Life-Years gained.

Objective

To test the external validity of a previously published mapping algorithm to transform the EORTC QLQ-C30 questionnaire responses into EQ-5D-derived utilities by predicting EQ-5D utilities from QLQ-C30 scores.

Study design and methods

Comparative retrospective data analysis of four multicentre, prospective clinical trials in Breast, Multiple Myeloma, Non-Hodgkin Lymphoma and Non-Small-Cell Lung cancer patients with, respectively, 219, 172, 132 and 172 patients. Regression analysis of individual pairs of EQ-5D and QLQ-C30 scores.

Results

Although the internal predictive power of a previously published mapping equation was high, its external validity when tested on a set of unrelated external data sets in other cancers proved to underestimate both the mean and variance of the mapped EQ-5D utilities. Furthermore, it appears that the relationship between QLQ-C30 scores and EQ-5D values is not stable across the different data sets.

Conclusions

Validation of the proposed algorithm in other external clinical data sets should be encouraged as well as the application of other more complex mapping methods to enhance accuracy of mapping. In the meanwhile, direct mapping from QLQ-C30 profiles to EQ-5D utilities using published algorithms should be performed with reservations.

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Notes

  1. Note: Utilities were transformed to disutilities because this results in a right-skewed distribution bounded by zero at the left. This in fact only changes the sign of the coefficients in the regressions and the size of the constant.

  2. OLS does not request a particular distribution of the dependent or independent variable. It does request, however, normality of the residuals with zero mean or more generally that “the mean of the distribution from which the disturbance term is drawn is zero” (Kennedy P, A Guide to Econometrics, 2003).

  3. Note: No formal econometric structural test based on individual data, like the Chow test or Wald test, could be performed, as the individual patient data are residing in different institutions, and no pooled data set could be constructed due to patient data confidentiality and ownership constraints.

Abbreviations

HRQOL:

Health-related quality of life

EORTC:

European organization for research and treatment of cancer

QALY:

Quality-adjusted life-years

MM:

Multiple myeloma

BR:

Breast cancer

NHL:

Non-Hodgkin lymphoma

NSCLC:

Non-small-cell lung cancer

HSQOL:

Global health status

MAE:

Mean average error

RMSE:

Root-mean-squared error

QOL:

Quality of life

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Acknowledgments

We would like to thank Dr Leighl from the Dept of Medicine, Division of Medical Oncology Princess Margaret Hospital, University of Toronto and Dr Mittmann from the Health Outcomes and PharmacoEconomic (HOPE) Research Centre, Sunnybrook Research Institute, University of Toronto for helping to access to the NSCLC data and Dr Uyl-De Groot from IMTA for providing us permission to use the MM and NHL data. We would also like to thank Dr Raymond Jang for his help in interpreting these data.

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Authors and Affiliations

  1. 14 rue Tir aux Pigeons, 1150, Brussels, Belgium

    Ralph Crott

  2. Institute for Medical Technology Assessment, Erasmus University Rotterdam, J-building—Campus Woudestein, PO Box 1738, 3000 DR, Rotterdam, The Netherlands

    Matthijs Versteegh & Carin Uyl-de-Groot

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  1. Ralph Crott
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  2. Matthijs Versteegh
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  3. Carin Uyl-de-Groot
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Correspondence to Ralph Crott.

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Crott, R., Versteegh, M. & Uyl-de-Groot, C. An assessment of the external validity of mapping QLQ-C30 to EQ-5D preferences. Qual Life Res 22, 1045–1054 (2013). https://doi.org/10.1007/s11136-012-0220-9

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