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

Irina Proskorovsky, Philip Lewis, Cathy D Williams, Karin Jordan, Charalampia Kyriakou, Jack Ishak, Faith E Davies, Irina Proskorovsky, Philip Lewis, Cathy D Williams, Karin Jordan, Charalampia Kyriakou, Jack Ishak, Faith E Davies

Abstract

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.

Figures

Figure 1
Figure 1
Observed and predicted EQ-5D utility from trimmed model with EORTC QLQ-C30 and QLQ-MY20.
Figure 2
Figure 2
Observed and predicted EQ-5D utility from trimmed model with EORTC QLQ-C30 only.

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Source: PubMed

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