BACKGROUND: Generic preference-based measures of health may not adequately cover the impact of some conditions. There is therefore increasing interest in developing condition-specific preference-based measures. OBJECTIVES: The purpose of this study was to estimate a preference-based measure from a condition-specific measure of health for urinary incontinence, the 21-item King's Health Questionnaire, for use in economic evaluation. METHODS: The King's Health Questionnaire (KHQ) was revised into a 5-dimensional health state classification amenable to valuation using items selected using psychometric evidence. Forty-nine states were valued using standard gamble by a representative sample of patients with urinary incontinence attending UK hospital outpatient clinics. Each respondent was asked to value 9 health states. Models have been estimated for predicting health state valuations for all 1024 states defined by the KHQ classification. The modeling had to cope with the clustering of data by respondent and its skewed distribution. RESULTS: In total, 110 usable interviews were obtained from 169 patients approached to participate in the study. These responders generated 959 health state valuations. Mean health state values ranged from 0.77 to 0.98. Models were estimated using mean health state values and random effects models of individual-level health state values. These models generated robust estimates of the "main effects," and in general, the results support the ordinality of the KHQ health state classification. There were problems modeling interaction effects, and a number of alternatives were explored. CONCLUSION: The recommended model for estimating a preference-based measure from the condition-specific KHQ is presented.
BACKGROUND: Generic preference-based measures of health may not adequately cover the impact of some conditions. There is therefore increasing interest in developing condition-specific preference-based measures. OBJECTIVES: The purpose of this study was to estimate a preference-based measure from a condition-specific measure of health for urinary incontinence, the 21-item King's Health Questionnaire, for use in economic evaluation. METHODS: The King's Health Questionnaire (KHQ) was revised into a 5-dimensional health state classification amenable to valuation using items selected using psychometric evidence. Forty-nine states were valued using standard gamble by a representative sample of patients with urinary incontinence attending UK hospital outpatient clinics. Each respondent was asked to value 9 health states. Models have been estimated for predicting health state valuations for all 1024 states defined by the KHQ classification. The modeling had to cope with the clustering of data by respondent and its skewed distribution. RESULTS: In total, 110 usable interviews were obtained from 169 patients approached to participate in the study. These responders generated 959 health state valuations. Mean health state values ranged from 0.77 to 0.98. Models were estimated using mean health state values and random effects models of individual-level health state values. These models generated robust estimates of the "main effects," and in general, the results support the ordinality of the KHQ health state classification. There were problems modeling interaction effects, and a number of alternatives were explored. CONCLUSION: The recommended model for estimating a preference-based measure from the condition-specific KHQ is presented.
Authors: Donna Rowen; John Brazier; Tracey Young; Sabine Gaugris; Benjamin M Craig; Madeleine T King; Galina Velikova Journal: Value Health Date: 2011 Jul-Aug Impact factor: 5.725
Authors: Jonathan W Kowalski; Anne M Rentz; John G Walt; Andrew Lloyd; Jeff Lee; Tracey A Young; Wen-Hung Chen; Neil M Bressler; Paul Lee; John E Brazier; Ron D Hays; Dennis A Revicki Journal: Qual Life Res Date: 2011-08-04 Impact factor: 4.147
Authors: Martina Garau; Koonal K Shah; Anne R Mason; Qing Wang; Adrian Towse; Michael F Drummond Journal: Pharmacoeconomics Date: 2011-08 Impact factor: 4.981
Authors: Philip Hykin; A Toby Prevost; Sobha Sivaprasad; Joana C Vasconcelos; Caroline Murphy; Joanna Kelly; Jayashree Ramu; Abualbishr Alshreef; Laura Flight; Rebekah Pennington; Barry Hounsome; Ellen Lever; Andrew Metry; Edith Poku; Yit Yang; Simon P Harding; Andrew Lotery; Usha Chakravarthy; John Brazier Journal: Health Technol Assess Date: 2021-06 Impact factor: 4.014