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Development of Conversion Functions Mapping the FACT-B Total Score to the EQ-5D-5L Utility Value by Three Linking Methods and Comparison with the Ordinary Least Square Method

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Abstract

Introduction

Health-related quality-of-life (HRQoL) measures are commonly mapped to a value that represents a utility for economic evaluation via regression models, which may lead to shrinkage of the variance.

Objectives

This study aimed to develop and compare conversion functions that map the Functional Assessment of Cancer Therapy—Breast (FACT-B) total score to the EuroQoL 5-Dimensions, 5-Levels (EQ-5D-5L) utility value via four methods.

Methods

We used the HRQoL scores of 238 Singapore patients with breast cancer to develop the conversion function for the equipercentile, linear equating, mean rank and ordinary least squares (OLS) methods. We compared the distributions of the observed values and the four sets of mapped values and performed regression analyses to assess whether the association with risk factors was preserved by utility values derived from mapping.

Results

At baseline, the observed EQ-5D-5L utility value had a mean ± standard deviation (SD) of 0.820 ± 0.152, and 24.8% of the respondents attained a value of 1. The OLS method (mean 0.820; SD 0.112; proportion 0%) better agreed with the observed data than the equipercentile (mean 0.831; SD 0.152; proportion 23.5%), linear equating (mean 0.814; SD 0.145; proportion 11.8%) and mean rank method (mean 0.821; SD 0.147; proportion 23.9%). The significance of association was preserved for all parameters involved in the regression analyses by the equipercentile and linear equating methods, but the mean rank and OLS methods were inconsistent with the observed data for one and two parameters, respectively.

Conclusion

The problem of shrinkage in the variance occurred in the OLS method, but it provided an unbiased estimate for the mean and better agreement. Among the other three linking methods, the mean rank method better described the distribution, whereas the equipercentile and linear equating methods better assessed the association with risk factors.

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Data Availability Statement

The datasets generated during and/or analysed during the current study are not publicly available because our informed consent form stated that the collected data would not be released to people other than the research team.

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Author information

Authors and Affiliations

  1. School of Public Health, The University of Hong Kong, 1/F Patrick Manson Building, 7 Sassoon Road, Pokfulam, Hong Kong

    Chun Fan Lee

  2. Department of Medical Oncology, National Cancer Center, Singapore, Singapore

    Raymond Ng

  3. Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore

    Nan Luo

  4. Center for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore

    Yin Bun Cheung

  5. Department of Biostatistics, Singapore Clinical Research Institute, Singapore, Singapore

    Yin Bun Cheung

  6. Center for Child Health Research, University of Tampere and Tampere University Hospital, Tampere, Finland

    Yin Bun Cheung

Authors
  1. Chun Fan Lee
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  2. Raymond Ng
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  3. Nan Luo
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  4. Yin Bun Cheung
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Contributions

CFL, NL and YBC designed the study. CFL analysed the data and drafted the manuscript. All authors interpreted the analysis results and critically reviewed and approved the manuscript.

Corresponding author

Correspondence to Chun Fan Lee.

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Funding

This study was funded by National Medical Research Council, Singapore (NMRC/EDG/0063/2009).

Ethical standards

This study was approved by the Singapore Health Services Institutional Review Board and has been performed in accordance with the ethical standards of the Declaration of Helsinki.

Informed consent

Written informed consent was provided by the patients who agreed to participate in this study.

Conflict of interest

CFL, RN, NL and YBC have no potential conflicts of interest.

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Lee, C.F., Ng, R., Luo, N. et al. Development of Conversion Functions Mapping the FACT-B Total Score to the EQ-5D-5L Utility Value by Three Linking Methods and Comparison with the Ordinary Least Square Method. Appl Health Econ Health Policy 16, 685–695 (2018). https://doi.org/10.1007/s40258-018-0404-8

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  • DOI: https://doi.org/10.1007/s40258-018-0404-8

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