Computing PROPr Utility Scores for PROMIS® Profile Instruments

• Published in: Value in health Vol. 23; no. 3; pp. 370 - 378
• Main Authors: Dewitt, Barry, Jalal, Hawre, Hanmer, Janel
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.03.2020; Elsevier Science Ltd
• Subjects: Activities of Daily Living; Adolescent; Adult; Aged; Aged, 80 and over; Anxiety - diagnosis; Anxiety - psychology; Bayes Theorem; Bayesian analysis; Clinical outcomes; Cognition; Cognitive ability; Cognitive functioning; Female; Health Status; Health Status Indicators; health utility; Humans; Linear analysis; Male; Mathematical models; Measurement; Mental Health; Middle Aged; Neural networks; Neural Networks, Computer; Pain; Pain Measurement; Patient Reported Outcome Measures; patient-reported outcomes; Predictive Value of Tests; PROMIS; PROPr; Psychometrics; Regression analysis; Reproducibility of Results; Validity; Young Adult; United States > US; patient-reported outcomes; PROPr; PROMIS; health utility
• ISSN: 1098-3015; 1524-4733
• DOI: 10.1016/j.jval.2019.09.2752

The Patient-Reported Outcomes Measurement Information System® (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. 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. 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. 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. •The Patient-Reported Outcomes Measurement Information System (PROMIS®) is a set of widely-used patient-reported outcomes measures. The PROMIS-Preference (PROPr) scoring system provides summary utility scores for PROMIS measurements, but it requires measurements from 7 PROMIS scales. Without all of those measurements, it has not been possible to produce a utility score.•We produce a method for computing a PROPr utility score for a set of standardized PROMIS questionnaires, the PROMIS Profile instruments, which are missing one of PROPr’s 7 required PROMIS domains, Cognitive Function–Abilities.