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Estimating a Minimal Important Difference for the EQ-5D-5L Utility Index in Dialysis Patients
Amanda N Siriwardana1, Anna T Hoffman2, Rachael L Morton3
1Renal and Metabolic Division, The George Institute for Global Health, Sydney, NSW, Australia; Sydney Medical School, University of Sydney, Sydney, NSW, Australia; Department of Renal Medicine, Royal North Shore Hospital, Sydney, NSW, Australia.
Objectives:
The EQ-5D-5L is a commonly used health-related quality of life instrument for evaluating interventions in patients receiving dialysis; however, the minimal important difference (MID) that constitutes a meaningful treatment effect for this population has not been established. This study aims to estimate the MID for the EQ-5D-5L utility index in dialysis patients.
Methods:
6-monthly EQ-5D-5L measurements were collected from adult dialysis patients between April 2017 and November 2020 at a renal network in Sydney, Australia. EQ-VAS and Integrated Palliative care Outcome Scale Renal symptom burden scores were collected simultaneously and used as anchors. MID estimates for the EQ-5D-5L utility index were derived using anchor-based and distribution-based methods.
Results:
A total of 352 patients with ≥1 EQ-5D-5L observation were included, constituting 1127 observations. Mean EQ-5D-5L utility index at baseline was 0.719 (SD ± 0.267), and mean EQ-5D-5L utility decreased over time by -0.017 per year (95% CI -0.029 to -0.006, P = .004). Using cross-sectional anchor-based methods, MID estimates ranged from 0.073 to 0.107. Using longitudinal anchor-based methods, MID for improvement and deterioration ranged from 0.046 to 0.079 and -0.111 to -0.048, respectively. Using receiver operating characteristic curves, MID for improvement and deterioration ranged from 0.037 to 0.122 and -0.074 to -0.063, respectively. MID estimates from distribution-based methods were consistent with anchor-based estimates.
Conclusions:
Anchor-based and distribution-based approaches provided EQ-5D-5L utility index MID estimates ranging from 0.034 to 0.134. These estimates can inform the target difference or "effect size" for clinical trial design among dialysis populations.
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