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Prediction of patient‐reported outcome measures via multivariate ordered probit models

The assessment of patient‐reported outcome measures (PROMs) is of central importance in many areas of research and public policy. Unfortunately, it is quite common for clinical studies to employ different PROMs, thus limiting the comparability of the evidence base that they contribute to. This issue is exacerbated by the fact that some national agencies are now explicit about which PROMs must be used to generate evidence in support of claims for reimbursement. The National Institute for Health and Care Excellence for England and Wales, for instance, has identified in EuroQoL‐5D, EQ‐5D, the PROM of choice, while accepting the use of a ‘mapping’ approach to predict EQ‐5D from other PROMs when EQ‐5D data have not been collected. Here we consider the problem of directly predicting EQ‐5D responses from ‘Short form 12', while recognizing both the likely dependence between the five dimensions of the EQ‐5D responses at the patient level, and the fact that the levels of each health dimension are naturally ordered. We carry out the analysis within a Bayesian framework. We also address the key problem of choosing an appropriate summary measure of agreement between predicted and actual results when analysing PROMs, with particular attention devoted to scoring rules.