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Developing preference-based health measures: using Rasch analysis to generate health state values

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Abstract

Purpose

Condition-specific measures may not always have independent items, yet existing techniques of developing health state utility values from these measures are inappropriate when items are not independent. This study develops methods for deriving and valuing health states for a condition-specific preference-based measure where items are not independent.

Methods

The analysis has three stages: firstly, Rasch analysis is used to develop a health state classification system from the Flushing Symptoms Questionnaire (FSQ) that is amenable to valuation and to identify a set of health states for valuation. Secondly, a valuation survey of the health states using time-trade-off (TTO) methods is conducted to elicit health state values. Finally, regression models are applied to map the relationship between mean TTO values and Rasch logit values. The model is then used to estimate health state values for all possible health states.

Results

Rasch models were fitted to 1,270 responders to the FSQ and a series of 16 health states were identified for the valuation exercise. An ordinary least squares model best described the relationship between mean TTO values and Rasch logit values (R 2 = 0.958; root mean square error = 0.042).

Conclusions

This study demonstrates how health state utility values can be mapped onto Rasch logit values in order to value all states defined by the FSQ, a condition-specific measure where items are not independent. This should significantly enhance research in this field.

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Abbreviations

CRE:

The Centre for Research and Evaluation

FSQ:

Flushing Symptoms Questionnaire

HRQL:

Health-related quality of life

IRT:

Item response theory

MSE:

Mean squared error

MVH:

Measurement and Valuation of Health

NS:

Not significant

OLS:

Ordinary least squares

PBM:

Preference-based measures

PSI:

Person separation index

QALY:

Quality adjusted life years

TTO:

Time-trade-off

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Acknowledgments

The authors gratefully acknowledge the help of Dr. Vasilisa Sazonov and Dr. Thomas Rhodes from Merck & Co., for their help in completing this study. This study was funded by Merck & Co. Inc and in particular by Global Outcomes Research & Reimbursement Department.

Author information

Authors and Affiliations

  1. School of Health and Related Research, University of Sheffield, Regent Court, 30 Regent Street, Sheffield, S1 4DA, UK

    Tracey A. Young, Donna Rowen & John E. Brazier

  2. Yorkshire and Humber Research Design Service, University of Sheffield, Sheffield, S1 4DA, UK

    Tracey A. Young

  3. Merck & Co. Inc., One Merck Drive, Whitehouse Station, NJ, 08889, USA

    Josephine Norquist

Authors
  1. Tracey A. Young
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  2. Donna Rowen
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  3. Josephine Norquist
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  4. John E. Brazier
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Correspondence to John E. Brazier.

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Young, T.A., Rowen, D., Norquist, J. et al. Developing preference-based health measures: using Rasch analysis to generate health state values. Qual Life Res 19, 907–917 (2010). https://doi.org/10.1007/s11136-010-9646-0

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  • DOI: https://doi.org/10.1007/s11136-010-9646-0

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