Use of Bayesian Markov Chain Monte Carlo Methods to Estimate EQ-5D Utility Scores from EORTC QLQ Data in Myeloma for Use in Cost-Effectiveness Analysis

Background. Patient-reported outcome measures are an important component of the evidence for health technology appraisal. Their incorporation into cost-effectiveness analyses (CEAs) requires conversion of descriptive information into utilities. This can be done by using bespoke utility algorithms. Otherwise, investigators will often estimate indirect utility models for the patient-reported outcome measures using off-the-shelf utility data such as the EQ-5D or SF-6D. Numerous modeling strategies are reported; however, to date, there has been limited utilization of Bayesian methods in this context. In this article, we examine the relative advantage of the Bayesian methods in relation to dealing with missing data, relaxing the assumption of equal variances and characterizing the uncertainty in the model predictions. Methods. Data from a large myeloma trial were used to examine the relationship between scores in each of the 19 domains of the European Organisation for Research and Treatment of Cancer (EORTC) QLQ-C30/QLQ-MY20 and the EQ-5D utility. Data from 1839 patients were divided 75%/25% between derivation and validation sets. A conventional ordinary least squares model assuming equal variance and a Bayesian model allowing unequal variance were estimated on complete cases. Two further models were estimated using conventional and Bayesian multiple imputation, respectively, using the full data set. Models were compared in terms of data fit, accuracy in model prediction, and characterization of uncertainty in model predictions. Conclusions. Mean EQ-5D utility weights can be estimated from the EORTC QLQ-C30/QLQ-MY20 for use in CEAs. Frequentist and Bayesian methods produced effectively identical models. However, the Bayesian models provide distributions describing the uncertainty surrounding the estimated utility values and are thus more suited informing analyses for probabilistic CEAs.