Mapping the EORTC QLQ-C30 and QLQ-H&N35 to the EQ-5D for head and neck cancer: Can disease-specific utilities be obtained?

PLoS One. 2019 Dec 13;14(12):e0226077. doi: 10.1371/journal.pone.0226077. eCollection 2019.

Abstract

Introduction: Innovations in head and neck cancer (HNC) treatment are often subject to economic evaluation prior to their reimbursement and subsequent access for patients. Mapping functions facilitate economic evaluation of new treatments when the required utility data is absent, but quality of life data is available. The objective of this study is to develop a mapping function translating the EORTC QLQ-C30 to EQ-5D-derived utilities for HNC through regression modeling, and to explore the added value of disease-specific EORTC QLQ-H&N35 scales to the model.

Methods: Data was obtained on patients with primary HNC treated with curative intent derived from two hospitals. Model development was conducted in two phases: 1. Predictor selection based on theory- and data-driven methods, resulting in three sets of potential predictors from the quality of life questionnaires; 2. Selection of the best out of four methods: ordinary-least squares, mixed-effects linear, Cox and beta regression, using the first set of predictors from EORTC QLQ-C30 scales with most correspondence to EQ-5D dimensions. Using a stepwise approach, we assessed added values of predictors in the other two sets. Model fit was assessed using Akaike and Bayesian Information Criterion (AIC and BIC) and model performance was evaluated by MAE, RMSE and limits of agreement (LOA).

Results: The beta regression model showed best model fit, with global health status, physical-, role- and emotional functioning and pain scales as predictors. Adding HNC-specific scales did not improve the model. Model performance was reasonable; R2 = 0.39, MAE = 0.0949, RMSE = 0.1209, 95% LOA of -0.243 to 0.231 (bias -0.01), with an error correlation of 0.32. The estimated shrinkage factor was 0.90.

Conclusions: Selected scales from the EORTC QLQ-C30 can be used to estimate utilities for HNC using beta regression. Including EORTC QLQ-H&N35 scales does not improve the mapping function. The mapping model may serve as a tool to enable cost-effectiveness analyses of innovative HNC treatments, for example for reimbursement issues. Further research should assess the robustness and generalizability of the function by validating the model in an external cohort of HNC patients.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Antineoplastic Agents / therapeutic use
  • Bayes Theorem
  • Emotions
  • Female
  • Head and Neck Neoplasms / drug therapy
  • Head and Neck Neoplasms / pathology
  • Head and Neck Neoplasms / psychology*
  • Health Status
  • Humans
  • Least-Squares Analysis
  • Male
  • Middle Aged
  • Models, Statistical*
  • Quality of Life*

Substances

  • Antineoplastic Agents

Associated data

  • Dryad/10.5061/dryad.qbzkh18cz

Grants and funding

AB and MS are supported by a non-restricted research grant from ATOS Medical Sweden contributing to the existing infrastructure for health-related quality of life research of the department of Head and Neck Oncology and Surgery. The funder provided support in the form of salaries for authors AB and MS, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.