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Condition-specific preference-based measures can offer utility data where they would not otherwise be available or where generic measures may lack sensitivity, although they lack comparability across conditions. This study aimed to develop an algorithm for estimating utilities from the short bowel syndrome health-related quality of life scale (SBS-QoL™).
SBS-QoL™ items were selected based on factor and item performance analysis of a European SBS-QoL™ dataset and consultation with 3 SBS clinical experts. Six-dimension health states were developed using 8 SBS-QoL™ items (2 dimensions combined 2 SBS-QoL™ items). SBS health states were valued by a UK general population sample (N = 250) using the lead-time time trade-off method. Preference weights or ‘utility decrements’ for each severity level of each dimension were estimated by regression models and used to develop the scoring algorithm.
Mean utilities for the SBS health states ranged from −0.46 (worst health state, very much affected on all dimensions) to 0.92 (best health state, not at all affected on all dimensions). The random effects model with maximum likelihood estimation regression had the best predictive ability and lowest root mean squared error and mean absolute error, and was used to develop the scoring algorithm.
The preference-weighted scoring algorithm for the SBS-QoL™ developed is able to estimate a wide range of utility values from patient-level SBS-QoL™ data. This allows estimation of SBS HRQL impact for the purpose of economic evaluation of SBS treatment benefits.
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- Economic evaluation in short bowel syndrome (SBS): an algorithm to estimate utility scores for a patient-reported SBS-specific quality of life scale (SBS-QoL™)
- Springer International Publishing