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

OBJECTIVES: The Roland Morris Questionnaire (RMQ) is a widely used health status measure for low back pain (LBP). It is not preference-based, and there are currently no established algorithms for mapping between the RMQ and preference-based health-related quality-of-life measures. Using data from randomized controlled trials of treatment for LBP, we sought to develop algorithms for mapping between RMQ scores and health utilities derived using either the EuroQol five-dimensional questionnaire (EQ-5D) or the six-dimensional health state short form (derived from Medical Outcomes Study 36-Item Short-Form Health Survey) (SF-6D).

METHODS: This study is based on data from the Back Skills Training Trial in which data were collected from 701 patients at baseline and subsequently at 3, 6, and 12 months postrandomization using a range of outcome measures, including the RMQ, the EQ-5D, and the Short Form 12 item Health Survey (SF-12) (from which SF-6D utilities can be derived). We used baseline trial data to estimate models using both direct and response mapping approaches to predict EQ-5D and SF-6D health utilities and dimension responses. A multistage model selection process was used to assess the predictive accuracy of the models. We then explored different techniques and mapping models that made use of repeated follow-up observations in the data. The estimated mapping algorithms were validated using external data from the UK Back Pain Exercise and Manipulation trial.

RESULTS: A number of models were developed that accurately predict health utilities in this context. The best performing model for RMQ to EQ-5D mapping was a beta regression with Bayesian quasi-likelihood estimation that included 24 dummy variables for RMQ responses, age, and sex as covariates (mean squared error 0.0380) based on repeated data. The model selected for RMQ to SF-6D mapping was a finite mixture model that included the overall RMQ score, age, sex, RMQ score squared, age squared, and an interaction term for age and RMQ score as covariates (mean squared error 0.0114) based on repeated data.

CONCLUSIONS: It is possible to reasonably predict EQ-5D and SF-6D health utilities from RMQ scores and responses using regression methods. Our regression equations provide an empirical basis for estimating health utilities when EQ-5D or SF-6D data are not available. They can be used to inform future economic evaluations of interventions targeting LBP.