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Mapping between the Roland Morris Questionnaire and generic preference-based measures.

Kamran A Khan1, Jason Madan1, Stavros Petrou1

  • 1Warwick Clinical Trials Unit, Division of Health Sciences, Warwick Medical School, University of Warwick, Coventry, UK.

Value in Health : the Journal of the International Society for Pharmacoeconomics and Outcomes Research
|September 20, 2014
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Summary
This summary is machine-generated.

This study developed algorithms to predict health utilities using the Roland Morris Questionnaire (RMQ) for low back pain (LBP). These models allow for health utility estimation when EQ-5D or SF-6D data are unavailable, aiding economic evaluations.

Keywords:
EQ-5DLBPRMQSF-6Dcross-walkinghealth utilitieshealth-related quality of lifelow back painmapping

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Area of Science:

  • Health Economics
  • Clinical Epidemiology
  • Patient-Reported Outcomes

Background:

  • The Roland Morris Questionnaire (RMQ) is a common measure for low back pain (LBP) but is not preference-based.
  • Preference-based measures like EQ-5D and SF-6D are crucial for health utility calculations in economic evaluations.
  • Algorithms for mapping RMQ scores to preference-based measures are lacking.

Purpose of the Study:

  • To develop and validate algorithms for mapping RMQ scores to health utilities derived from EQ-5D and SF-6D.
  • To enable the estimation of health utilities from existing RMQ data for LBP patients.
  • To support economic evaluations of LBP interventions.

Main Methods:

  • Utilized data from 701 LBP patients from the Back Skills Training Trial.
  • Employed direct and response mapping approaches using baseline and repeated follow-up data.
  • Validated mapping algorithms using external data from the UK Back Pain Exercise and Manipulation trial.

Main Results:

  • Developed accurate regression models for predicting EQ-5D and SF-6D health utilities from RMQ scores.
  • The best RMQ to EQ-5D model used beta regression with 24 dummy variables, age, and sex (MSE 0.0380).
  • The optimal RMQ to SF-6D model was a finite mixture model including RMQ score, age, sex, and their interactions (MSE 0.0114).

Conclusions:

  • Regression methods can effectively predict EQ-5D and SF-6D health utilities from RMQ data.
  • The derived regression equations provide a basis for estimating health utilities when EQ-5D or SF-6D data are absent.
  • These algorithms can inform future economic evaluations for LBP interventions.