A Discrete Choice Experiment to Derive Health Utilities for Aromatic L-Amino Acid Decarboxylase (AADC) Deficiency.

IF 1.8 Q3 HEALTH CARE SCIENCES & SERVICES
Patient Related Outcome Measures Pub Date : 2021-05-12 eCollection Date: 2021-01-01 DOI:10.2147/PROM.S294628
Adam B Smith, Andria Hanbury, Jennifer A Whitty, Katharina Buesch
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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.

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通过离散选择实验得出芳香族 L-氨基酸脱羧酶 (AADC) 缺乏症的健康效用。
目的:为芳香族 L-氨基酸脱羧酶(AADC)缺乏症等罕见病症提取健康效用值是一项挑战。AADC 缺乏症非常罕见,而且这种遗传性疾病通常发生在年幼的儿童身上,这意味着无法从儿童或其父母/照顾者那里得出可靠的效用值。为了提供健康效用值,需要采用其他方法,如离散选择实验(DCE)。本研究的目的是利用离散选择实验来得出缺乏反式脂肪肝的健康效用值:方法:DCE 由来自英国代表性样本的小组参与者在线完成。DCE 包括 6 个 AADC 缺乏症属性(2-6 级):行动能力、肌肉无力、眼球震颤、喂养能力、认知障碍和尖叫。这些属性是根据已发表的文献、临床医生的意见、家长访谈和专家意见确定的。采用正交设计向参与者展示了 10 组选择,其中包括一项重复任务以评估选择的一致性。在进行 DCE 之前,向参与者展示了 5 个健康状态小故事。这些小故事用于激发时间权衡(TTO)效用。对 DCE 数据进行了多叉 logit 模型估计。最差/最佳健康状态的时间权衡效用被用作锚,将间接的DCE部分价值效用转换为健康效用:共有 1596 名参与者完成了 DCE。大多数人(70.7%)对重复选择任务做出了一致的回答;只有 1.7%(27 人)在每组选择中总是选择相同的备选方案。评估了五个模型。在所有模型中都出现了一种偏好反转("无辅助坐姿"/"有辅助站姿");在最终模型中,这两种移动水平系数被设定为相等。重标度效用介于 0.494 至 0.7279 之间,分别对应最差(633233)和最佳(111111)的健康状况:结论:通过 DCE 得出了 AADC 缺乏症的健康效用。结论:通过 DCE 得出了 AADC 缺乏症的健康效用,这些效用将用于 AADC 缺乏症治疗的成本效益模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Patient Related Outcome Measures
Patient Related Outcome Measures HEALTH CARE SCIENCES & SERVICES-
自引率
4.80%
发文量
27
审稿时长
16 weeks
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