Estimation of a Preference-Based Index from a Condition-Specific Measure: The King's Health Questionnaire
- 19 July 2007
- journal article
- research article
- Published by SAGE Publications in Medical Decision Making
- Vol. 28 (1), 113-126
- https://doi.org/10.1177/0272989x07301820
Abstract
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.Keywords
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