Mapping Studies to Estimate Health-State Utilities From Nonpreference-Based Outcome Measures: A Systematic Review on How Repeated Measurements are Taken Into Account

Gonçalves, Ana Sofia Oliveira; Werdin, Sophia; Kurth, Tobias; Пантели, Димитра

doi:10.1016/j.jval.2022.09.2477

Cited by 1 publication

(1 citation statement)

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“…In contrast to the other models, we did not estimate random intercepts for ALDVMM, as this option is currently neither available in the R nor in the Stata package. We did, however, estimate cluster-robust standard errors, as suggested by Oliveira Gonçalves et al [ 32 ]. Finally, we estimated our mapping algorithm using a mixed-effects regression forest (MERF).…”

Section: Methodsmentioning

confidence: 99%

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

Steiner¹,

Bokemeyer²,

Stargardt³

2023

Eur J Health Econ

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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.

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Section: Methodsmentioning

confidence: 99%