Mapping from SIBDQ to EQ-5D-5L for patients with inflammatory bowel disease.

Autor: Steiner IM; Hamburg Center for Health Economics, University of Hamburg, Esplanade 36, 20354, Hamburg, Germany. isa.maria.steiner@uni-hamburg.de., Bokemeyer B; Interdisziplinäres Crohn Colitis Centrum Minden, Märchenweg 17, 32429, Minden, Germany., Stargardt T; Hamburg Center for Health Economics, University of Hamburg, Esplanade 36, 20354, Hamburg, Germany.
Jazyk: angličtina
Zdroj: The European journal of health economics : HEPAC : health economics in prevention and care [Eur J Health Econ] 2024 Apr; Vol. 25 (3), pp. 539-548. Date of Electronic Publication: 2023 Jun 27.
DOI: 10.1007/s10198-023-01603-9
Abstrakt: Objective: Clinical studies commonly use disease-specific measures to assess patients' health-related quality of life. However, economic evaluation often requires preference-based utility index scores to calculate cost per quality-adjusted life-year (QALY). When utility index scores are not directly available, mappings are useful. To our knowledge, no mapping exists for the Short Inflammatory Bowel Disease Questionnaire (SIBDQ). Our aim was to develop a mapping from SIBDQ to the EQ-5D-5L index score with German weights for inflammatory bowel disease (IBD) patients.
Methods: We used 3856 observations of 1055 IBD patients who participated in a randomised controlled trial in Germany on the effect of introducing regular appointments with an IBD nurse specialist in addition to standard care with biologics. We considered five data availability scenarios. For each scenario, we estimated different regression and machine learning models: linear mixed-effects regression, mixed-effects Tobit regression, an adjusted limited dependent variable mixture model and a mixed-effects regression forest. We selected the final models with tenfold cross-validation based on a model subset and validated these with observations in a validation subset.
Results: For the first four data availability scenarios, we selected mixed-effects Tobit regressions as final models. For the fifth scenario, mixed-effects regression forest performed best. Our findings suggest that the demographic variables age and gender do not improve the mapping, while including SIBDQ subscales, IBD disease type, BMI and smoking status leads to better predictions.
Conclusion: We developed an algorithm mapping SIBDQ values to EQ-5D-5L index scores for different sets of covariates in IBD patients. It is implemented in the following web application: https://www.bwl.uni-hamburg.de/hcm/forschung/mapping.html .
(© 2023. The Author(s).)
Databáze: MEDLINE
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