Mapping EORTC QLQ-C30 and QLQ-MY20 to EQ-5D in patients with multiple myeloma

Health and Quality of Life Outcomes

Faith E Davies⁶  Jack Ishak²  Charalampia Kyriakou³  Karin Jordan⁴  Cathy D Williams¹  Philip Lewis⁵  Irina Proskorovsky² 

[1] Centre for Clinical Haematology, Nottingham University Hospital, Nottingham, UK;Evidera, 7575 Trans-Canada Highway, Suite 500, H4T 1V6 Montreal, QC, Canada;Department of Haematology, Northwick Park and Royal Free Hospitals, London, UK;Klinik und Poliklinik für Innere Medizin IV, Universitätsklinikum Halle (Saale), Halle, Germany;Celgene GmbH, Munich, Germany;Institute of Cancer Research and Royal Marsden Hospital, Surrey, UK

关键词: Utility values;    Oncology;    Multiple myeloma;    Health-related quality of life;    EQ-5D;    EORTC QLQ-MY20;    EORTC QLQ-C30;   

Others  :  1164580
DOI  :  10.1186/1477-7525-12-35

 received in 2013-01-25, accepted in 2014-02-26,  发布年份 2014

【 摘 要 】

Background

In oncology, health-related quality of life (HRQoL) data are often collected using disease-specific patient questionnaires while generic, patient-level utility data required for health economic modeling are often not collected.

Methods

We developed a mapping algorithm for multiple myeloma that relates HRQoL scores from the European Organization for Research and Treatment of Cancer (EORTC) questionnaires QLQ-C30 and QLQ-MY20 to a utility value from the European QoL-5 Dimensions (EQ-5D) questionnaire. Data were obtained from 154 multiple myeloma patients who had participated in a multicenter cohort study in the UK or Germany. All three questionnaires were administered at a single time point. Scores from all 19 domains of the QLQ-C30 and QLQ-MY20 instruments were univariately tested against EQ-5D values and retained in a multivariate regression model if statistically significant. A 10-fold cross-validation model selection method was also used as an alternative testing means. Two models were developed: one based on QLQ-C30 plus QLQ-MY20 scores and one based on QLQ-C30 scores alone. Adjusted R-squared, correlation coefficients, and plots of observed versus predicted EQ-5D values were presented for both models.

Results

Mapping revealed that Global Health Status/QoL, Physical Functioning, Pain, and Insomnia were significant predictors of EQ-5D utility values. Similar results were observed when QLQ-MY20 scores were excluded from the model, except that Emotional Functioning and became a significant predictor and Insomnia was no longer a significant predictor. Adjusted R-squared values were of similar magnitude with or without inclusion of QLQ-MY20 scores (0.70 and 0.69, respectively), suggesting that the EORTC QLQ-MY20 adds little in terms of predicting utility values in multiple myeloma.

Conclusions

This algorithm successfully mapped EORTC HRQoL data onto EQ-5D utility in patients with multiple myeloma. Current mapping will aid in the analysis of cost-effectiveness of novel therapies for this indication.

【 授权许可】

   
2014 Proskorovsky et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

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