Adam B Smith1, Andria Hanbury1, Jennifer A Whitty2, Katharina Buesch3. 1. York Health Economics Consortium, University of York, York, UK. 2. Norwich Medical School, University of East Anglia, Norwich, UK. 3. PTC Therapeutics, Zug, 6300, Switzerland.
Abstract
PURPOSE: Deriving health utilities for rare medical conditions such as aromatic L-amino acid decarboxylase (AADC) deficiency poses challenges. The rarity of AADC deficiency and the fact that this genetic condition often presents in very young children means that robust utility values cannot be derived from the child or their parent/caregiver. Alternative approaches, eg, discrete choice experiments (DCE), are required in order to provide health utilities. The aim of the study was to generate health utilities for AADC deficiency using a DCE. METHODS: The DCE was completed online by panel participants from a UK representative sample. The DCE comprised 6 AADC deficiency attributes (2-6 levels): mobility, muscle weakness, oculogyric crises, feeding ability, cognitive impairment and screaming. These were identified from published literature, clinician input, parent interviews and expert opinion. Participants were presented with 10 choice sets specified using an orthogonal design, including a repeat task to evaluate choice consistency. Participants were presented with 5 health state vignettes prior to the DCE. These were used to elicit time trade-off (TTO) utilities. Multinomial logit models were estimated for the DCE data. The TTO utilities for the worst/best health states were used as anchors to convert indirect DCE part-worth utilities to health utilities. RESULTS: A total of 1596 participants completed the DCE. The majority (70.7%) gave consistent responses to the repeated choice task; only 1.7% (27) always chose the same alternative for every choice set. Five models were evaluated. There was one preference reversal ("sitting unaided"/"standing with assistance") occurring in all models; these two mobility level coefficients were set to be equal in the final model. Rescaled utilities ranged from 0.494 to 0.7279, corresponding to the worst (633233) and best (111111) health states. CONCLUSION: Health utilities were derived for AADC deficiency through a DCE. These will be used for a cost-effectiveness model of an AADC deficiency treatment.
PURPOSE: Deriving health utilities for rare medical conditions such as aromatic L-amino acid decarboxylase (AADC) deficiency poses challenges. The rarity of AADC deficiency and the fact that this genetic condition often presents in very young children means that robust utility values cannot be derived from the child or their parent/caregiver. Alternative approaches, eg, discrete choice experiments (DCE), are required in order to provide health utilities. The aim of the study was to generate health utilities for AADC deficiency using a DCE. METHODS: The DCE was completed online by panel participants from a UK representative sample. The DCE comprised 6 AADC deficiency attributes (2-6 levels): mobility, muscle weakness, oculogyric crises, feeding ability, cognitive impairment and screaming. These were identified from published literature, clinician input, parent interviews and expert opinion. Participants were presented with 10 choice sets specified using an orthogonal design, including a repeat task to evaluate choice consistency. Participants were presented with 5 health state vignettes prior to the DCE. These were used to elicit time trade-off (TTO) utilities. Multinomial logit models were estimated for the DCE data. The TTO utilities for the worst/best health states were used as anchors to convert indirect DCE part-worth utilities to health utilities. RESULTS: A total of 1596 participants completed the DCE. The majority (70.7%) gave consistent responses to the repeated choice task; only 1.7% (27) always chose the same alternative for every choice set. Five models were evaluated. There was one preference reversal ("sitting unaided"/"standing with assistance") occurring in all models; these two mobility level coefficients were set to be equal in the final model. Rescaled utilities ranged from 0.494 to 0.7279, corresponding to the worst (633233) and best (111111) health states. CONCLUSION: Health utilities were derived for AADC deficiency through a DCE. These will be used for a cost-effectiveness model of an AADC deficiency treatment.
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