Computing PROPr Utility Scores for PROMIS® Profile Instruments

Objectives: The Patient-Reported Outcomes Measurement Information System (R) (PROMIS) Profile instruments measure health status on 8 PROMIS domains. The PROMIS-Preference (PROPr) score provides a preference-based summary score for health states defined by 7 PROMIS domains. The Profile and PROPr share 6 domains; PROPr has 1 unique domain (Cognitive Function-Abilities), and the Profile has 2 unique domains (Anxiety and Pain Intensity). We produce an equation for calculating PROPr utility scores with Profile data. Methods: We used data from 3982 members of US online survey panels who have scores on all 9 PROMIS domains. We used a 70%/30% split for model fit/validation. Using root-mean-square error and mean error on the utility scale, we compared models for predicting the missing Cognitive Function score via (A) the population average; (B) a score representing excellent cognitive function; (C) a score representing poor cognitive function; (D) a score predicted from linear regression of the 8 profile domains; and (E) a score predicted from a Bayesian neural network of the 8 profile domains. Results: The mean errors in the validation sample on the PROPr scale (which ranges from -0.022 to 1.00) for the models were: (A) 0.025, (B) 0.067, (C) -0.23, (D) 0.018, and (E) 0.018. The root-mean-square errors were: (A) 0.097, (B) 0.12, (C) 0.29, (D) 0.095, and (E) 0.094. Conclusion: Although the Bayesian neural network had the best root-mean-square error for producing PROPr utility scores from Profile instruments, linear regression performs almost as well and is easier to use. We recommend the linear model for producing PROPr utility scores for PROMIS Profiles.