Zhang Ying, Anh Ho Thi Quynh, Terris-Prestholt Fern, Quaife Matthew, de Bekker-Grob Esther, Vickerman Peter, Ong Jason J
School of Translational Medicine, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Victoria, Australia.
Melbourne Sexual Health Centre, Alfred Health, Melbourne, Victoria, Australia.
EClinicalMedicine. 2024 Dec 16;79:102965. doi: 10.1016/j.eclinm.2024.102965. eCollection 2025 Jan.
Discrete choice experiments (DCEs) are increasingly used to inform the design of health products and services. It is essential to understand the extent to which DCEs provide reliable predictions outside of experimental settings in real-world decision-making situations. We aimed to compare the prediction accuracy of stated preferences with real-world choices, as modelled from DCE data.
We searched six databases for health-related studies that used DCE to assess external validity and reported on predicted versus real-world choices, up to July 2024. A generalised linear mixed model was used for a meta-analysis to jointly pool the sensitivity and specificity. Heterogeneity was assessed using the statistic, and sources of heterogeneity using meta-regression. This study is registered with PROSPERO (CRD42023451545).
We identified 14 relevant studies, of which 10 were included in the meta-analysis. Most studies were conducted in high-income countries (11/14, 79%) from the European region (9/14, 64%) and analysed using mixed logit models (5/14, 36%). Pooled sensitivity and specificity estimates were 89% (95% CI:77-95, = 97%) and 52% (95% CI:32-72, = 95%), respectively. The area under the SROC curve (AUC) was 0.81 (95% CI:0.77-0.84). Our meta-regression found that DCEs for prevention-related choices had higher sensitivity than treatment-related choices. DCEs conducted under clinical settings and analysed using the heteroskedastic multinomial logit model, incorporating systematic preference heterogeneity and random opt-out utility, had higher specificity than non-clinical settings and alternative models.
DCEs are valuable for capturing health-related preferences and possess reasonable external validity to predict health-related behaviours, particularly for opt-in choices. Contextual factors (e.g., type of intervention, study setting, analysis method) influenced the predictive accuracy.
JJO is supported by an Australian National Health and Medical Research Council Emerging Leadership Investigator Grant (GNT1193955). EBG is supported by the Dutch Research Council (NWO-Talent-Scheme-Vidi-Grant No, 09150171910002). YZ is supported by an Australian Government Research Training Program (RTP) scholarship.
离散选择实验（DCEs）越来越多地用于为健康产品和服务的设计提供信息。了解DCEs在现实世界决策情境中的实验设置之外能在多大程度上提供可靠预测至关重要。我们旨在比较基于DCE数据建模的陈述性偏好与现实世界选择的预测准确性。
我们检索了六个数据库，以查找截至2024年7月使用DCE评估外部有效性并报告预测选择与现实世界选择的健康相关研究。使用广义线性混合模型进行荟萃分析，以联合汇总敏感性和特异性。使用Q统计量评估异质性，并使用meta回归分析异质性来源。本研究已在PROSPERO（CRD42023451545）注册。
我们确定了14项相关研究，其中10项纳入了荟萃分析。大多数研究在欧洲地区的高收入国家进行（11/14，79%），并使用混合逻辑模型进行分析（5/14，36%）。汇总的敏感性和特异性估计分别为89%（95%CI：77 - 95，I² = 97%）和52%（95%CI：32 - 72，I² = 95%）。SROC曲线下面积（AUC）为0.81（95%CI：0.77 - 0.84）。我们的meta回归发现，与预防相关选择的DCEs比与治疗相关选择的具有更高的敏感性。在临床环境下进行并使用异方差多项逻辑模型分析的DCEs，纳入系统偏好异质性和随机退出效用，比非临床环境和替代模型具有更高的特异性。
DCEs对于捕捉与健康相关的偏好很有价值，并且在预测与健康相关的行为方面具有合理的外部有效性，特别是对于选择加入的选择。背景因素（如干预类型、研究环境、分析方法）影响预测准确性。
JJO得到澳大利亚国家卫生与医学研究委员会新兴领导力研究员资助（GNT1193955）。EBG得到荷兰研究委员会（NWO - 人才计划 - Vidi资助号，09150171910002）的支持。YZ得到澳大利亚政府研究培训计划（RTP）奖学金的支持。
