Testing mapping algorithms of the cancer-specific EORTC QLQ-C30 onto EQ-5D in malignant mesothelioma

Health and Quality of Life Outcomes

Nicholas A Maskell¹  Clare E Hooper¹  Anna Morley¹  Matthijs M Versteegh²  Donna Rowen³  David T Arnold¹ 

[1]Academic Respiratory Unit, School of Clinical Sciences, University of Bristol, Bristol, UK

[2]Institute for Medical Technology Assessment, Erasmus University of Rotterdam, Rotterdam, Netherlands

[3]School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK

关键词: QALY;    Health technology assessment;    Mesothelioma;    Mapping;    QLQ-C30;    EQ-5D;   

Others  :  1133896
DOI  :  10.1186/s12955-014-0196-y

 received in 2014-09-02, accepted in 2014-12-17,  发布年份 2015

【 摘 要 】

Background

In order to estimate utilities for cancer studies where the EQ-5D was not used, the EORTC QLQ-C30 can be used to estimate EQ-5D using existing mapping algorithms. Several mapping algorithms exist for this transformation, however, algorithms tend to lose accuracy in patients in poor health states. The aim of this study was to test all existing mapping algorithms of QLQ-C30 onto EQ-5D, in a dataset of patients with malignant pleural mesothelioma, an invariably fatal malignancy where no previous mapping estimation has been published.

Methods

Health related quality of life (HRQoL) data where both the EQ-5D and QLQ-C30 were used simultaneously was obtained from the UK-based prospective observational SWAMP (South West Area Mesothelioma and Pemetrexed) trial. In the original trial 73 patients with pleural mesothelioma were offered palliative chemotherapy and their HRQoL was assessed across five time points. This data was used to test the nine available mapping algorithms found in the literature, comparing predicted against observed EQ-5D values. The ability of algorithms to predict the mean, minimise error and detect clinically significant differences was assessed.

Results

The dataset had a total of 250 observations across 5 timepoints. The linear regression mapping algorithms tested generally performed poorly, over-estimating the predicted compared to observed EQ-5D values, especially when observed EQ-5D was below 0.5. The best performing algorithm used a response mapping method and predicted the mean EQ-5D with accuracy with an average root mean squared error of 0.17 (Standard Deviation; 0.22). This algorithm reliably discriminated between clinically distinct subgroups seen in the primary dataset.

Conclusions

This study tested mapping algorithms in a population with poor health states, where they have been previously shown to perform poorly. Further research into EQ-5D estimation should be directed at response mapping methods given its superior performance in this study.

【 授权许可】

   
2015 Arnold et al.; licensee BioMed Central.

【 图 表 】

Figure 1.

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