The psychometric properties of a self-administered, open-source, web-based tool for valuing metastatic spinal cord compression health states

Abstract

Objectives: Internet market research panels are often used as a substitute for general population samples for ex ante utility valuation. Typically, custom utility valuation tools that have not undergone psychometric evaluation are used. This study aims to determine the psychometric properties of a customizable opensource internet-based self-directed utility valuation tool the (Self-directed Online Assessment of Preferences; SOAP) module for metastatic epidural spinal cord compression health states. Methods: Individuals accompanying patients to the emergency department waiting room were recruited into this study. Participants made SOAP metastatic epidural spinal cord compression health state valuations in the waiting room, and 48 hours later at home. Validity, agreement reliability, and responsiveness were measured by logical consistency of responses, Smallest Detectable Change, the Interclass Correlation Coefficient, and Guyatt\u27s Responsiveness Index respectively. Results: Of 285 participants who completed utility valuations, only 113 (39.6%) completed the re-test. Of these 113 participants, 92 (81.4%) provided valid responses on the first test, and 75 (66.4%) provided valid responses on the test and re-test. Agreement for all groups of health states was adequate since their Smallest Detectable Change was less than the Minimally Clinically Important Difference. The mean Interclass Correlation Coefficient s for all health states were greater than 0.8 indicating at least substantial reliability. Guyatt\u27s Responsiveness Indices all exceeded 0.80, indicating high level of responsiveness. Conclusions: The SOAP metastatic epidural spinal cord compression module is a valid, reproducible and responsive tool for obtaining ex ante utilities. This tool can now be used to obtain general population valuations of metastatic epidural spinal cord compression health states. Additional modules could be developed to facilitate decision making for other diseases

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