Comparison of the EQ-5D-5L and the patient-reported outcomes measurement information system preference score (PROPr) in the United States.

BACKGROUND: In contrast to prior research, our study presents longitudinal comparisons of the EQ-5D-5L and Patient-Reported Outcomes Measurement Information System (PROMIS) preference (PROPr) scores. This fills a gap in the literature, providing a much-needed understanding of these preference-based measures and their applications in healthcare research. Furthermore, our study provides equations to estimate one measure from the other, a tool that can significantly facilitate comparisons across studies. METHODS: We administered a health survey to 4,098 KnowledgePanel® members living in the United States. A subset of 1,256 (82% response rate) with back pain also completed the six-month follow-up survey. We then conducted thorough cross-sectional and longitudinal analyses of the two measures, including product-moment correlations between scores, associations with demographic variables, and health conditions. To estimate one measure from the other, we used ordinary least squares (OLS) regression with the baseline data from the general population. RESULTS: The correlation between the EQ-5D-5L and PROPr scores was 0.69, but the intraclass correlation was only 0.34 because the PROPr had lower (less positive) mean scores on the 0 (dead) to 1 (perfect health) continuum than the EQ-5D-5L. The associations between the two preference measures and demographic variables were similar at baseline. The product-moment correlation between unstandardized beta coefficients for each preference measure regressed on 22 health conditions was 0.86, reflecting similar patterns of unique associations. Correlations of change from baseline to 6 months in the two measures with retrospective perceptions of change were similar. Adjusted variance explained in OLS regressions predicting one measure from the other was 48%. On average, the predicted values were within a half-standard deviation of the observed EQ-5D-5L and PROPr scores. The beta-binomial regression model slightly improved over the OLS model in predicting the EQ-5D-5L from the PROPr but was equivalent to the OLS model in predicting the PROPr. CONCLUSION: Despite substantial mean differences, the EQ-5D-5L and PROPr have similar cross-sectional and longitudinal associations with other variables. We provide the OLS regression equations for use in cost-effectiveness research and meta-analyses. Future studies are needed to compare these measures with different conditions and interventions to provide more information on their relative validity.

Mapping of the PROMIS global health measure to the PROPr in the United States.

BACKGROUND: The Patient-Reported Outcomes Measurement and Information System (PROMIS®) global health items (global-10) yield physical and mental health scale scores and the PROMIS-Preference (PROPr) scoring system estimated from PROMIS domain scores (e.g., PROMIS-29 + 2) produces a single score anchored by 0 (dead or as bad as being dead) to 1 (full health). A link between the PROMIS global-10 and the PROPr is needed. METHODS: The PROMIS-29 + 2 and the PROMIS global-10 were administered to 4102 adults in the Ipsos KnowledgePanel in 2022. The median age was 52 (range 18-94), 50% were female, 70% were non-Hispanic White, and 64% were married or living with a partner. The highest level of education completed for 26% of the sample was a high school degree or general education diploma and 44% worked full-time. We estimated correlations of the PROPr with the PROMIS global health items and the global physical and mental health scales. We examined the adjusted R2 and estimated correlations between predicted and observed PROPr scores. RESULTS: Product-moment correlations between the PROMIS global health items and the PROPr ranged from 0.50 to 0.63. The PROMIS global physical health and mental health scale scores correlated 0.74 and 0.60, respectively, with the PROPr. The adjusted R2 in the regression of the PROPr on the PROMIS global health items was 64%. The equated PROPr preference scores correlated (product-moment) 0.80 (n = 4043; p < 0.0001) with the observed PROPr preference scores, and the intra-class correlation (two-way random effects model) was 0.80. The normalized mean absolute error (NMAE) was 0.45 (SD = 0.43). The adjusted R2 in the OLS regression of the PROPr on the PROMIS global health scales was 59%. The equated PROPr preference scores correlated (product-moment) was 0.77 (n = 4046; p < 0.0001) with the observed PROPr preference scores, and the intra-class correlation was 0.77. The NMAE was 0.49 (SD = 0.45). CONCLUSIONS: Regression equations provide a reasonably accurate estimate of the PROPr preference-based score from the PROMIS global health items or scales for group-level comparisons. These estimates facilitate cost-effectiveness research and meta-analyses. The estimated PROPr scores are not accurate enough for individual-level applications. Future evaluations of the prediction equations are needed.

Scale agreement, ceiling and floor effects, construct validity, and relative efficiency of the PROPr and EQ-5D-3L in low back pain patients.

BACKGROUND: The PROMIS Preference score (PROPr) is a new health state utility (HSU) score that aims to comprehensively incorporate the biopsychosocial model of health and apply favorable psychometric properties from the descriptive PROMIS system to HSU measurements. However, minimal evidence concerning comparisons to the EQ-5D-3L and the PROPr's capability to differentiate clinical severity are available. Therefore, the aim of this study was to compare the PROPr to the EQ-5D-3L in terms of scale agreement, ceiling/floor effects, distribution, construct validity, discriminatory power, and relative efficiency (RE) in terms of the Oswestry Disability Index (ODI) for patients with low back pain (LBP). METHODS: We used intra-class correlation coefficients (ICC) and Bland-Altman plots to compare the PROPr and EQ-5D-3L with regared to scale agreement in a cross-sectional routine sample of LBP patients. For distribution, we used the Pearson's coefficient for skewness and for ceiling/floor effects, a 15%-top/bottom threshold. For convergent validity, we used Pearson's correlation coefficients. For known-groups validity, we applied a linear regression with interaction terms (predictors sex, age, and ODI level) and an analysis of variance (ANOVA). For discriminatory power, we calculated the effect size (ES) using Cohen's d and the ratio of the area under the receiver-operating characteristics curves (AUROC-ratio = AUROCPROPr/AUROCEQ-5D-3L). RE was measured using the ratio of F-values (RE = FPROPr/FEQ-5D-3L). RESULTS: Of 218 LBP patients, 50.0% were female and the mean age was 61.8 years. The mean PROPr (0.20, 95%CI: 0.18; 0.22) and EQ-5D-3L scores (0.55, 95%CI: 0.51; 0.58) showed low agreement (d = 0.35, p < 0.001; ICC 0.27, 95%CI: -0.09; 0.59). The PROPr's distribution was positively skewed, whereas the EQ-5D-3L's was negative. Neither tool showed ceiling/floor effects, but all EQ-5D-3L dimensions did. Pearson correlation was r = 0.66 (95%CI: 0.58; 0.73). Differences were invariant to sex and age but not to ODI severity: ESEQ-5D-3L > ESPROPr and RE < 1 in higher ODI severity; ESEQ-5D-3L < ESPROPr and RE > 1 in lower ODI severity. AUROC-ratios did not show significant differences in terms of ODI severity. CONCLUSIONS: All PROPr and EQ-5D-3L biopsychosocial dimensions of health showed impairment in LPB patients. The capability of EQ-5D-3L and PROPr to differentiate ODI levels depends on ODI severity. Joint application of both tools may provide additional information.

Utilities Estimated from PROMIS Scales for Cost-Effectiveness Analyses in Stroke.

BACKGROUND: The EQ-5D and Health Utilities Index Mark 3 (HUI-3) are preference-based measures used in cost-effectiveness studies. The Patient Reported Outcomes Measurement Information System (PROMIS) Preference scoring system (PROPr) is a new preference-based measure. In addition, algorithms were previously developed to map PROMIS Global Health (PROMIS-GH) items to HUI-3 using linear equating (HUILE) and 3-level EQ-5D using linear (EQ5DLE). We sought to evaluate and compare estimated utilities based on PROPr and PROMIS-GH in adult stroke survivors. METHODS: We performed a retrospective cohort study of adults diagnosed with 1 of ischemic stroke, intracerebral hemorrhage, or subarachnoid hemorrhage seen in an outpatient clinic between 2015 and 2019. Patients completed PROMIS scales and other measures. We computed a modified version of PROPr (mPROPr) and compared the distributional characteristics and correlations with stroke outcomes for mPROPr, HUILE, and EQ5DLE. RESULTS: T toal of 4,159 stroke survivors (average age 62.7 ± 14.7 y, 48.4% female, 77.6% ischemic stroke) were included. Mean utility estimates for mPROPr, EQ5DLE, and HUILE were 0.333 ± 0.244, 0.739 ± 0.201, and 0.544 ± 0.301, respectively. Correlations between the modified Rankin Scale and each of mPROPr and HUILE were both -0.48 and -0.43 for EQ5DLE. Regression analyses indicated that mPROPr scores may be too low for stroke patients in good health and that EQ5DLE scores may be too high for stroke patients in poor health. CONCLUSIONS: All 3 PROMIS-based utilities were associated with measures of stroke disability and severity, but the distributions of utilities were very different. Our study highlights the problem cost-effectiveness researchers face of valuing health states with certainty. For researchers using utilities estimated from PROMIS scales, our study indicates that mapping PROMIS-GH item scores to HUI-3 via linear equating may be most appropriate in stroke patients. HIGHLIGHTS: A new preference-based measure has been developed from the Patient Reported Outcomes Measurement Information System (PROMIS), known as the PROMIS-Preference (PROPr) scoring system, and published equations mapping PROMIS Global Health (PROMIS-GH) items to the Health Utilities Index Mark 3 (HUI-3) and EQ-5D-3L are available for use in cost-effectiveness studies.Our study provides distributional characteristics and comparisons of utilities estimated using a modified version of PROPr and equations mapping PROMIS-GH items to EQ-5D-3L and HUI-3 in a sample of stroke survivors.The results of our study show large differences in the distributions of utilities estimated using the different health state measures, and these differences highlight the ongoing difficulty researchers face in valuing health states with certainty.

Health state utility differed systematically in breast cancer patients between the EORTC QLU-C10D and the PROMIS Preference Score.

BACKGROUND AND OBJECTIVES: The EORTC Quality of Life Utility Core 10 Dimensions (QLU-C10D) and the Patient-Reported Outcome Measurement Information System Preference Score (PROPr) are new health state utility (HSU) scores for quality-adjusted life years in cost-effectiveness analyses. Both are expected to measure HSU more comprehensively than existing measures in cancer patients by including cancer-related health domains such as fatigue. The aim of this study is to compare both scores in a sample of breast cancer patients. METHODS: We collected QLU-C10D and PROPr from 291 patients 90 days after treatment in the outpatient clinic of the breast cancer center at Charité - University Medicine Berlin between June 2018 and April 2021. We assessed both scores' convergent and known-groups validity, agreement, and ceiling and floor effects. RESULTS: The mean QLU-C10D score [0.71, 95% confidence interval (CI) 0.69-0.74] and the mean PROPr score (0.43, 95% CI 0.41-0.46) differed systematically (0.28, 95% CI 0.27-0.30) and showed fair agreement (intraclass correlation coefficient 0.46, 95% CI 0.32-0.57). The Pearson correlation coefficient was 0.83 (95% CI 0.79-0.86). Both scores showed similar discrimination across known groups of age, treatment, cancer stage, marital status, and education. The QLU-C10D showed relevant ceiling effects. CONCLUSION: QLU-C10D and PROPr measure HSU differently as a result of different utility models. The choice between QLU-C10D and PROPr should be informed by context, population, disease, and treatment.

Psychometric properties of the PROMIS Preference score (PROPr) in patients with rheumatological and psychosomatic conditions.

BACKGROUND: The PROMIS Preference score (PROPr) is a new generic preference-based health-related quality of life (HRQoL) score that can be used as a health state utility (HSU) score for quality-adjusted life years (QALYs) in cost-utility analyses (CUAs). It is the first HSU score based on item response theory (IRT) and has demonstrated favorable psychometric properties in first analyses. The PROPr combines the seven PROMIS domains: cognition, depression, fatigue, pain, physical function, sleep disturbance, and ability to participate in social roles and activities. It was developed based on preferences of the US general population. The aim of this study was to validate the PROPr in a German inpatient sample and to compare it to the EQ-5D. METHODS: We collected PROPr and EQ-5D-5L data from 141 patients undergoing inpatient treatment in the rheumatology and psychosomatic departments. We evaluated the criterion and convergent validity, and ceiling and floor effects of the PROPr and compared those characteristics to those of the EQ-5D. RESULTS: The mean PROPr (0.26, 95% CI: 0.23; 0.29) and the mean EQ-5D (0.44, 95% CI: 0.38; 0.51) scores differed significantly (d = 0.18, p < 0.001). Compared to the EQ-5D, the PROPr scores were less scattered across the measurement range which has resulted in smaller confidence intervals of the mean scores. The Pearson correlation coefficient between the two scores was r = 0.72 (p < 0.001). Both scores showed fair agreement with an Intraclass Correlation Coefficient (ICC) of 0.48 (p < 0.05). The PROPr and EQ-5D demonstrated similar discrimination power across sex, age, and conditions. While the PROPr showed a floor effect, the EQ-5D showed a ceiling effect. CONCLUSION: The PROPr measures HSU considerably lower than the EQ-5D as a result of different construction, anchors and measurement ranges. Because QALYs derived with the EQ-5D are widely considered state-of-the-art, application of the PROPr for QALY measurements would be problematic.

Evaluation of PROMIS Preference Scoring System (PROPr) in Patients Undergoing Hemodialysis or Kidney Transplant.

BACKGROUND AND OBJECTIVES: A preference-based health utility score (PROPr) can be calculated using Patient-Reported Outcomes Measurement Information System domain scores. We assessed the construct validity of PROPr among patients treated with KRT (hemodialysis or kidney transplant). DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: We performed a secondary analysis of data collected in multicenter, cross-sectional studies of adults treated with KRT, recruited between April 2016 to March 2020 in Toronto, Canada. All participants provided informed consent. The outcome was the PROPr score. Coadministered outcome variables included the Short-Form Six-Domain (SF-6D) and EuroQol Five-Domain Five-Level (EQ-5D-5L) scores. Socioeconomic and clinical variables included age, sex, diabetes, eGFR, serum albumin, hemoglobin, KRT, and Charlson Comorbidity Index. Construct validity was assessed through correlations between PROPr and SF-6D or EQ-5D-5L, and associations between PROPr and other exposure variables. Health-condition impact estimates (coefficients for health conditions compared with a referent category, e.g., dialysis versus kidney transplant) were calculated using multivariable linear regression. RESULTS: The mean (SD) age of the 524 participants was 57 (17) years, 58% were male, and 45% were White. Median (interquartile range) score was 0.39 (0.24-0.58) for PROPr, 0.69 (0.58-0.86) for SF-6D, and 0.85 (0.70-0.91) for EQ-5D-5L. Large correlations were observed between PROPr versus SF-6D (0.79; 95% confidence interval [95% CI], 0.76 to 0.82) and EQ-5D-5L (0.71; 95% CI, 0.66 to 0.75). Both PROPr and the other utility indices demonstrated health-condition impact in the expected direction. For example, the estimate for PROPr was -0.17 (95% CI, -0.13 to -0.21) for dialysis (versus kidney transplant), -0.05 (95% CI, -0.11 to 0.01; P=0.08) for kidney transplant recipients with an eGFR of <45 versus ≥45 ml/min per 1.73 m2, and -0.28 (95% CI, -0.22 to -0.33) for moderate/severe versus no/mild depressive symptoms. CONCLUSIONS: Our results support the validity of PROPr among patients treated with KRT.

Cross-sectional validation of the PROMIS-Preference scoring system by its association with social determinants of health.

PURPOSE: PROMIS-Preference (PROPr) is a generic, societal, preference-based summary score that uses seven domains from the Patient-Reported Outcomes Measurement Information System (PROMIS). This report evaluates construct validity of PROPr by its association with social determinants of health (SDoH). METHODS: An online panel survey of the US adult population included PROPr, SDoH, demographics, chronic conditions, and four other scores: the EuroQol-5D-5L (EQ-5D-5L), Health Utilities Index (HUI) Mark 2 and Mark 3, and the Short Form-6D (SF-6D). Each score was regressed on age, gender, health conditions, and a single SDoH. The SDoH coefficient represents the strength of its association to PROPr and was used to assess known-groups validity. Convergent validity was evaluated using Pearson correlations between different summary scores and Spearman correlations between SDoH coefficients from different summary scores. RESULTS: From 4142 participants, all summary scores had statistically significant differences for variables related to education, income, food and financial insecurity, and social interactions. Of the 42 SDoH variables tested, the number of statistically significant variables was 27 for EQ-5D-5L, 17 for HUI Mark 2, 23 for HUI Mark 3, 27 for PROPr, and 27 for SF-6D. The average SDoH coefficients were - 0.086 for EQ-5D-5L, - 0.039 for HUI Mark 2, - 0.063 for HUI Mark 3, - 0.064 for PROPr, and - 0.037 for SF-6D. Despite the difference in magnitude across the measures, Pearson correlations were 0.60 to 0.76 and Spearman correlations were 0.74 to 0.87. CONCLUSIONS: These results provide evidence of construct validity supporting the use of PROPr monitor population health in the general US population.

Computing PROPr Utility Scores for PROMIS® Profile Instruments.

OBJECTIVES: 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. 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.