Payakachat Nalin, Tilford J Mick, Kuhlthau Karen A, van Exel N Job, Kovacs Erica, Bellando Jayne, Pyne Jeffrey M, Brouwer Werner B F
Division of Pharmaceutical Evaluation and Policy, University of Arkansas for Medical Sciences, Little Rock, Arkansas.
Autism Res. 2014 Dec;7(6):649-63. doi: 10.1002/aur.1409. Epub 2014 Sep 25.
Comparative effectiveness of interventions for children with autism spectrum disorders (ASDs) that incorporates costs is lacking due to the scarcity of information on health utility scores or preference-weighted outcomes typically used for calculating quality-adjusted life years (QALYs). This study created algorithms for mapping clinical and behavioral measures for children with ASDs to health utility scores. The algorithms could be useful for estimating the value of different interventions and treatments used in the care of children with ASDs. Participants were recruited from two Autism Treatment Network sites. Health utility data based on the Health Utilities Index Mark 3 (HUI3) for the child were obtained from the primary caregiver (proxy-reported) through a survey (N = 224). During the initial clinic visit, proxy-reported measures of the Child Behavior Checklist, Vineland II Adaptive Behavior Scales, and the Pediatric Quality of Life Inventory 4.0 (start measures) were obtained and then merged with the survey data. Nine mapping algorithms were developed using the HUI3 scores as dependent variables in ordinary least squares regressions along with the start measures, the Autism Diagnostic Observation Schedule, to measure severity, child age, and cognitive ability as independent predictors. In-sample cross-validation was conducted to evaluate predictive accuracy. Multiple imputation techniques were used for missing data. The average age for children with ASDs in this study was 8.4 (standard deviation = 3.5) years. Almost half of the children (47%) had cognitive impairment (IQ ≤ 70). Total scores for all of the outcome measures were significantly associated with the HUI3 score. The algorithms can be applied to clinical studies containing start measures of children with ASDs to predict QALYs gained from interventions.
由于缺乏通常用于计算质量调整生命年（QALY）的健康效用评分或偏好加权结果的信息，目前尚缺乏纳入成本的自闭症谱系障碍（ASD）儿童干预措施的比较有效性研究。本研究创建了将ASD儿童的临床和行为测量映射到健康效用评分的算法。这些算法可能有助于评估用于ASD儿童护理的不同干预措施和治疗的价值。研究参与者从两个自闭症治疗网络站点招募。通过一项调查（N = 224）从主要照顾者（代理报告）处获得基于健康效用指数Mark 3（HUI3）的儿童健康效用数据。在初次门诊就诊时，获取代理报告的儿童行为检查表、文兰适应行为量表第二版和儿童生活质量量表4.0（起始测量），然后将其与调查数据合并。使用HUI3评分作为普通最小二乘回归中的因变量，连同起始测量、自闭症诊断观察量表来测量严重程度、儿童年龄和认知能力作为独立预测变量，开发了九种映射算法。进行样本内交叉验证以评估预测准确性。对缺失数据使用多重填补技术。本研究中ASD儿童的平均年龄为8.4岁（标准差 = 3.5）。几乎一半的儿童（47%）有认知障碍（智商≤70）。所有结果测量的总分与HUI3评分显著相关。这些算法可应用于包含ASD儿童起始测量的临床研究，以预测从干预措施中获得的QALY。
