OBJECTIVES: To characterize the differences in utility scores (dUTY) among four commonly used preference-based Health-Related Quality of Life instruments, to evaluate the potential impact of these differences on cost-utility analyses (CUA), and to determine if sociodemographic/clinical factors influenced the magnitude of these differences. METHODS: Consenting adult Chinese, Malay and Indian subjects in Singapore were interviewed using Singapore English, Chinese, Malay or Tamil versions of the EQ-5D, Health Utilities Index Mark 2 (HUI2) and Mark 3 (HUI3), and SF-6D. Agreement between instruments was assessed using Bland-Altman (BA) plots. Changes in incremental cost-utility ratio (ICUR) from dUTY were investigated using eight hypothetical decision trees. The influence of sociodemographic/clinical factors on dUTY between instrument pairs was studied using multiple linear regression (MLR) models for English-speaking subjects (circumventing structural zero issues). RESULTS: In 667 subjects (median age 48 years, 59% female), median utility scores ranged from 0.80 (95% confidence interval [CI] 0.80, 0.85) for the EQ-5D to 0.89 (95% CI 0.88, 0.89) for the SF-6D. BA plots: Mean differences (95% CI) exceeded the clinically important difference (CID) of 0.04 for four of six pairwise comparisons, with the exception of the HUI2/EQ-5D (0.03, CI: 0.02, 0.04) and SF-6D/HUI2 (0.02, CI: 0.006, 0.02). Decision trees: The ICER ranged from $94,661/QALY (quality-adjusted life-year; 6.3% difference from base case) to 100,693 dollars/QALY (0.3% difference from base case). MLR: Chronic medical conditions, marital status, and Family Functioning Measures scores significantly (P-value < 0.05) influenced dUTY for several instrument pairs. CONCLUSION: Although CIDs in utility measurements were present for different preference-based instruments, the impact of these differences on CUA appeared relatively minor. Chronic medical conditions, marital status, and family functioning influenced the magnitude of these differences.
OBJECTIVES: To characterize the differences in utility scores (dUTY) among four commonly used preference-based Health-Related Quality of Life instruments, to evaluate the potential impact of these differences on cost-utility analyses (CUA), and to determine if sociodemographic/clinical factors influenced the magnitude of these differences. METHODS: Consenting adult Chinese, Malay and Indian subjects in Singapore were interviewed using Singapore English, Chinese, Malay or Tamil versions of the EQ-5D, Health Utilities Index Mark 2 (HUI2) and Mark 3 (HUI3), and SF-6D. Agreement between instruments was assessed using Bland-Altman (BA) plots. Changes in incremental cost-utility ratio (ICUR) from dUTY were investigated using eight hypothetical decision trees. The influence of sociodemographic/clinical factors on dUTY between instrument pairs was studied using multiple linear regression (MLR) models for English-speaking subjects (circumventing structural zero issues). RESULTS: In 667 subjects (median age 48 years, 59% female), median utility scores ranged from 0.80 (95% confidence interval [CI] 0.80, 0.85) for the EQ-5D to 0.89 (95% CI 0.88, 0.89) for the SF-6D. BA plots: Mean differences (95% CI) exceeded the clinically important difference (CID) of 0.04 for four of six pairwise comparisons, with the exception of the HUI2/EQ-5D (0.03, CI: 0.02, 0.04) and SF-6D/HUI2 (0.02, CI: 0.006, 0.02). Decision trees: The ICER ranged from $94,661/QALY (quality-adjusted life-year; 6.3% difference from base case) to 100,693 dollars/QALY (0.3% difference from base case). MLR: Chronic medical conditions, marital status, and Family Functioning Measures scores significantly (P-value < 0.05) influenced dUTY for several instrument pairs. CONCLUSION: Although CIDs in utility measurements were present for different preference-based instruments, the impact of these differences on CUA appeared relatively minor. Chronic medical conditions, marital status, and family functioning influenced the magnitude of these differences.
Authors: Dennis W Raisch; Patricia Feeney; David C Goff; K M Venkat Narayan; Patrick J O'Connor; Ping Zhang; Don G Hire; Mark D Sullivan Journal: Cardiovasc Diabetol Date: 2012-07-11 Impact factor: 9.951