Developing a health state classification system from NEWQOL for epilepsy using classical psychometric techniques and Rasch analysis: a technical report

Aims: Resource allocation amongst competing health care interventions is informed by evidence of both clinical- and cost-effectiveness. Cost-utility analysis is increasingly used to assess cost effectiveness through the use of Quality Adjusted Life Years (QALYs). This requires health state values. Generic measures of health related quality of life (HRQL) are usually used to produce these values, but there are concerns about their relevance and sensitivity in epilepsy. This study develops a health state classification system for epilepsy from the NEWQOL battery, a validated questionnaire measuring QoL in epilepsy. The classification system will be amenable to valuation for calculating QALYs. Methods: Factor and other psychometric analyses were undertaken to investigate the factor structure of the battery, and assess the validity and responsiveness of the items. These analyses were used alongside Rasch analysis to select the dimensions included in the classification system, and the items used to represent each domain. Analysis was carried out on a trial dataset of patients with epilepsy (n=1611). Rasch and factor analysis were performed on one half of the sample and validated on the remaining half. Dimensions and items were selected that performed well across all analyses. Results: The battery was found to demonstrate reliability and validity but responsiveness across time periods for many of the items was low. A six dimension classification system was developed: worry about seizures, depression, memory, cognition, stigmatism and control, each with four response levels. Conclusions: It is feasible to develop a health state classification system from a battery of instruments using a combination of classical psychometric, factor and Rasch analysis. This is the first condition-specific health state classification developed for epilepsy and the next stage will produce preference weights to enable the measure to be used in cost-utility analysis.