Clustering of EORTC QLQ-C30 health-related quality of life scales across several cancer types: Validation study.

Abstract

RESULTS

The dataset consisted of 20,066 patients pooled across 17 cancer types. Overall, three main clusters were identified (R² = 0.61); physical-cluster included role-functioning, physical-functioning, social-functioning, fatigue, pain, and global-health status; psychological-cluster included emotional-functioning, cognitive-functioning, and insomnia; gastro-intestinal-cluster included nausea/vomiting and appetite loss. The results were consistent across different levels of disease severity, socio-demographic and clinical characteristics with minor variations by cancer type. Global-health status was found to be strongly linked to the scales included in the physical-functioning-related cluster.

METHODS

Pre-defined criteria for successful validation were three main clusters should emerge with a minimum R-squared value of 0.51 using pooled baseline-data. A cluster analysis was performed on the 15 QLQ-C30 HRQoL-scales in the overall dataset, as well as by cancer type and selected patient characteristics to examine the robustness of the results.

CONCLUSION

This study successfully validated prior findings by Martinelli (2011): the QLQ-C30 scales are interrelated and can be grouped into three main clusters. Knowing how these multidimensional HRQoL scales are related to each other can help clinicians and patients with cancer in managing symptom burden, guide policymakers in defining social-support plans and inform selection of HRQoL scales in future clinical trials.

INTRODUCTION

The European Organisation for Research and Treatment of Cancer Quality of Life Core Questionnaire (EORTC QLQ-C30) measures 15 health-related quality of life (HRQoL) scales relevant to the disease and treatment of patients with cancer. A study by Martinelli (2011) demonstrated that these scales could be grouped into three main clusters: physical, psychological and gastrointestinal. This study aims to validate Martinelli's findings in an independent dataset and evaluate whether these clusters are consistent across cancer types and patient characteristics.