Coventry, UK – Low back pain (LBP) is a major public health problem in western industrialised societies. To make the best use of limited resources, planning of health care interventions is important. Models that translate patient outcomes into health utilities are valuable for decision makers planning these interventions.
In the study, “Mapping between the Roland Morris Questionnaire and Generic Preference-Based Measures,” published in Value in Health, researchers from the United Kingdom used mapping techniques, accepted statistical methods for defining a relationship between two health outcome measures, to generate EQ-5D and SF-6D health utilities from Roland Morris Questionnaire (RMQ) data. A wide variety of statistical models were explored in the estimation of the mapping algorithms and validated using external data.
A number of models were developed that predict health utilities in this context and the best performing models were a Beta regression model and a finite mixture model.
Study co-author Stavros Petrou, PhD, from the University of Warwick says, “This research shows that it is possible to reasonably predict EQ-5D and SF-6D health utilities from RMQ scores and responses using regression methods. These results can be used to inform utility estimation within future economic evaluations and aid decision makers.”