Mapping algorithms from QLQ-C30 to EQ-5D utilities: no firm ground to stand on yet

Abstract

Aim: Over the last years several mapping or cross-walking algorithms for deriving utilities from QLQ-C30 scores have been published. However their external predictive accuracy has not yet been systematically compared. Methods: We tested the external validity of previously published mapping algorithms to transform the European Organization for Research and Treatment of Cancer QLQ-C30 questionnaire responses to EQ-5D derived Utilities. Results: When applied to different data sets, the currently published mapping showed a large variation between algorithms of the values of the mapped utilities, a low accuracy of the mapping compared to the observed EQ-5D utilities and no consistent performance between competing algorithms. Discussion: Therefore direct mapping from QLQ-C30 profiles to EQ-5D utilities using published algorithms should be viewed cautiously.

Keywords::

• cancer
• cross-walking
• EORTC
• EQ-5D
• mapping
• preference
• QLQ-C30
• utility

Acknowledgement

We would like to thank Dr Leight from the Department 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 the NSCLC data.

We would also like to thank Dr Raymond Jang for his help in interpreting the NSCLC data.

Financial & competing interests disclosure

The author has no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

No writing assistance was utilized in the production of this manuscript.

Key issues

• Mapping algorithms are being developed in increasing numbers to derive health utilities (HUs) from health-related quality-of-life data in cancer but have rarely been tested for their external accuracy in unrelated data sets.

• Seven mapping algorithms for generating EQ-5D Health Utilities from the European Organization for Research and Treatment of Cancer QLQ-C30 cancer quality-of-life questionnaire were applied to two unrelated cancer data sets and compared with the original EQ-5D utilities in those two validation sets.

• Published mapping algorithms varied widely in their accuracy in predicting EQ-5D health utilities and were inconsistent between the two validation data sets.

• All algorithms under- or overstated the mean utility by 0.05 points at least and underestimated the sample variance.

• Low utilities below 0.50 were more subject to inacurrate estimation.

• Several algorithms also overestimated the upper ceiling utility of one.

• Use of published mapping algorithms for generating utilities from the QLQ-C30 questionnaire should be used with caution and corrected for underestimation of variance.