Mapping from Disease-Specific Measures to Health-State Utility Values in Individuals with Migraine

• Published in: Value in health Vol. 15; no. 3; pp. 485 - 494
• Main Authors: Gillard, Patrick J., PharmD, MS, Devine, Beth, PharmD, MBA, PhD, Varon, Sepideh F., PhD, Liu, Lei, Sullivan, Sean D., PhD
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.05.2012
• Subjects: Adult; Algorithms; chronic migraine; Correlation coefficients; episodic migraine; EQ-5D; Female; Health Status; HIT-6; Humans; Internal Medicine; Male; Mapping; Middle Aged; Migraine; Migraine Disorders - psychology; MSQ; Quality of Life; Regression Analysis; Surveys and Questionnaires; United States; utility; Validation; Young Adult; United States; EQ-5D; mapping; episodic migraine; HIT-6; utility; MSQ; chronic migraine
• ISSN: 1098-3015; 1524-4733
• DOI: 10.1016/j.jval.2011.12.007

Abstract Objective The objective of this study was to develop empirical algorithms that estimate health-state utility values from disease-specific quality-of-life scores in individuals with migraine. Methods Data from a cross-sectional, multicountry study were used. Individuals with episodic and chronic migraine were randomly assigned to training or validation samples. Spearman's correlation coefficients between paired EuroQol five-dimensional (EQ-5D) questionnaire utility values and both Headache Impact Test (HIT-6) scores and Migraine-Specific Quality-of-Life Questionnaire version 2.1 (MSQ) domain scores (role restrictive, role preventive, and emotional function) were examined. Regression models were constructed to estimate EQ-5D questionnaire utility values from the HIT-6 score or the MSQ domain scores. Preferred algorithms were confirmed in the validation samples. Results In episodic migraine, the preferred HIT-6 and MSQ algorithms explained 22% and 25% of the variance ( R2 ) in the training samples, respectively, and had similar prediction errors (root mean square errors of 0.30). In chronic migraine, the preferred HIT-6 and MSQ algorithms explained 36% and 45% of the variance in the training samples, respectively, and had similar prediction errors (root mean square errors 0.31 and 0.29). In episodic and chronic migraine, no statistically significant differences were observed between the mean observed and the mean estimated EQ-5D questionnaire utility values for the preferred HIT-6 and MSQ algorithms in the validation samples. Conclusions The relationship between the EQ-5D questionnaire and the HIT-6 or the MSQ is adequate to use regression equations to estimate EQ-5D questionnaire utility values. The preferred HIT-6 and MSQ algorithms will be useful in estimating health-state utilities in migraine trials in which no preference-based measure is present.