A Scoring Algorithm for Deriving Utility Values from the Neuro-QoL for Patients with Multiple Sclerosis.

Abstract

The Neuro-QoL is a standardized approach to assessing health-related quality of life in people with neurological conditions, including multiple sclerosis (MS). Item banks were developed with item response theory (IRT) methodology so items are calibrated along a continuum of each construct. The purpose of this study was to develop a preference-based scoring algorithm for the Neuro-QoL to derive utilities that could be used in economic modeling. With input from neurologists, 6 Neuro-QoL domains were selected based on relevance to MS and used to define health states for a utility elicitation study in the United Kingdom. General population participants and individuals with MS valued the health states and completed questionnaires (including Neuro-QoL short forms). The Neuro-QoL Utility Scoring System (NQU) was derived based on multi-attribute utility theory using data from the general population sample. Single-attribute disutility functions for 6 Neuro-QoL domains were estimated using isotonic regression with linear interpolation and then combined with a multiplicative model. NQU validity was assessed using MS participant data. Interviews were completed with 203 general population participants (50.2% female; mean age = 45.0 years) and 62 participants with MS (62.9% female; mean age = 46.1 years). Mean (SD) NQU scores were 0.94 (0.06) and 0.82 (0.13) for the general population and MS samples, respectively. The NQU demonstrated known-groups validity by differentiating among subgroups categorized based on level of disability. The NQU demonstrated convergent validity via correlations with generic measures (0.66 and 0.63 with EQ-5D-5L and Health Utilities Index Mark 3, respectively; both P < 0.001). With the NQU, utilities can be derived from any MS treatment group, subgroup, or patient sample who completes items from 6 Neuro-QoL domains. Because the Neuro-QoL is frequently used with MS patients, the NQU greatly expands the options for quantifying outcomes in cost-utility analyses conducted to inform allocation of resources for MS treatment.