Mapping EQ-5D-5L Score From SGRQ in Patients with Asthma and/or COPD in NOVELTY - Jackson D; Quinton A; Zhang F; Müllerová H; Janson C; Sadatsafavi M (Pragmatic and Observational Research)
Dan Jackson,1 Anna Quinton,2 Fanni Zhang,3 Hana Müllerová,4 Christer Janson,5 Mohsen Sadatsafavi6 1Statistical Innovation Group, AstraZeneca, Cambridge, UK; 2Global Health Economics and Payer Evidence, BioPharmaceuticals Business Unit, AstraZeneca, Cambridge, UK; 3Statistical Innovation Group, AstraZeneca, Gaithersburg, MD, USA; 4Respiratory Evidence Strategy, BioPharmaceuticals Medical, AstraZeneca, Cambridge, UK; 5Department of Medical Sciences, Respiratory, Allergy and Sleep Research, Uppsala University, Uppsala, Sweden; 6Respiratory Evaluation Sciences Program, Collaboration for Outcomes Research and Evaluation, Faculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, BC, CanadaCorrespondence: Dan Jackson, Statistical Innovation, AstraZeneca, Academy House, 136 Hills Road, Cambridge, CB2 8PA, UK, Email daniel.jackson1@astrazeneca.comPurpose: The St George’s Respiratory Questionnaire (SGRQ) measures health status in obstructive airways disease. Starkie et al proposed an algorithm for mapping the SGRQ to EQ-5D-5L, a preference-based utility measure, in chronic obstructive pulmonary disease (COPD) (Value Health 2011;14:354– 60); only SGRQ total score, its squared value, and sex were included as covariates. We aimed to determine if including additional covariates could improve the performance of this algorithm type and whether amendments were required to extend this mapping to asthma or asthma+COPD.Patients and Methods: SGRQ and EQ-5D-5L were measured from a large, global, prospective, longitudinal study in asthma and/or COPD (NOVELTY; NCT02760329). We fitted six longitudinal linear mixed models to the development sample (baseline and Year 1 data), with EQ-5D-5L as the response variable. Each model had a different combination of covariates. Mixed model repeated measures methodology was used to enable the accommodation of within-patient correlation among measurements. Restricted maximum likelihood and an unstructured covariance matrix were used to fit all models. Performance (mean square errors [MSE]) was evaluated relative to the Starkie et al algorithm in the validation sample (Year 2 and Year 3 data).Results: A total of 6813 patients (asthma: 3546; asthma+COPD: 872; COPD: 2395) with available EQ-5D-5L and SGRQ data were included at baseline. MSEs indicated good performance, were similar across models (Year 2: 0.0302– 0.0308 [45– 46% variance explained]; Year 3: 0.0272– 0.0277 [47– 48% variance explained]), and were modestly smaller than those obtained by Starkie et al (Year 2: 0.0340; Year 3: 0.0296). Performance was similar across models in the asthma and COPD subgroups.Conclusion: Including additional covariates and SGRQ domains resulted in similar model performance to Starkie et al, suggesting their covariates are adequate for mapping in asthma and/or COPD. NOVELTY coefficients broaden the population with chronic airways disease for whom this mapping can be applied.Keywords: health status, utility mapping, longitudinal analysis, algorithm, economic modeling, quality of life