Supplementary figures: Mapping the EORTC QLQ-C30 onto the EQ-5D-5L index for patients with paroxysmal nocturnal hemoglobinuria in France

<p dir="ltr"><b>These are peer-reviewed supplementary materials for the article '</b><b>Mapping the EORTC QLQ-C30 onto the </b><b>EQ-5D-5L index for patients with </b><b>paroxysmal nocturnal hemoglobinuria in </b><b>France</b><b>' published in the</b><b> </b><b><i>Journal of Comparative Effectiveness Research</i></b><b>.</b></p><ul><li><b>Supplementary </b><b>Figure 1: </b>Markov model diagram.</li><li><b>Supplementary Figure 2:. </b>Histogram of EORTC QLQ-C30 subscale scores.</li><li><b>Supplementary Figure 3: </b>Histogram of EQ-5D-5L domain scores.</li><li><b>Supplementary Figure 4: </b>Model performance.</li><li><b>Supplementary Figure 5: </b>Estimated utilities by visit and treatment.</li><li><b>Supplementary Table 1: </b>Types of regression models.</li><li><b>Supplementary Table 2: </b>Tobit model results.</li><li><b>Supplementary Table 3: </b>Health-state utilities by model</li></ul><p dir="ltr"><b>Aim: </b>To map patient-level data collected on the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC) QLQ-C30 to EQ-5D-5L data for estimating health-state utilities in patients with paroxysmal nocturnal hemoglobinuria (PNH).<b> Materials & methods:</b> European cross-sectional PNH patient survey data populated regression models mapping EORTC QLQ-C30 domains (covariates: sex and baseline age) to utilities calculated with the EQ-5D-5L French value set. A genetic algorithm allowed selection of the best-fitting between a set of models with and without interaction terms. We validated the selected algorithm using EQ-5D-5L utilities converted from EORTC QLQ-C30 data collected in the PEGASUS phase III, randomized controlled trial of pegcetacoplan versus eculizumab in adults with PNH. <b>Results:</b> Selected through the genetic algorithm, the ordinary least squares model without interactions provided highly stable results across study visits (mean [±SD] utilities 0.58 [±0.42] to 0.89 [±0.10]), and showed the best predictive validity. Conclusion: The new PNH EQ-5D-5L direct mapping developed using a genetic algorithm enabled calculation of reliable health-state utility data required for cost–utility analysis in health technology assessments supporting treatments of PNH.</p>