Underestimation of Uncertainties in Health Utilities Derived from Mapping Algorithms Involving Health-Related Quality-of-Life Measures: Statistical Explanations and Potential Remedies

Objectives. Mapping algorithms are being developed in increasing numbers to derive health utilities (HUs) from health-related quality-of-life (HRQOL) data. However, the variances of the mapping-derived HUs are observed to be smaller than those of the actual HUs. Methods. Two reasons are proposed: 1) the presence of important unmeasured predictors leading to a high degree of unex-plained variance and 2) ignoring that the regression coef-ficients are random variables themselves. We derive 3 variance estimators of HUs to account for these causes: 1) R2-adjusted estimator, 2) parametric estimator, and 3) nonparametric estimator. We tested these estimators using a simulated dataset and a real dataset involving the EQ-5D-3L and University of Washington Quality of Life ques-tionnaire for patients with head and neck cancers