Prediction accuracy of discrete choice experiments in health-related research: a systematic review and meta-analysis

作者：Ying Zhang, Thi Quynh Anh Ho, Fern Terris‐Prestholt, Matthew Quaife, Esther W. de Bekker‐Grob, Peter Vickerman, Jason J. Ong · 发表于：EClinicalMedicine · 年份：2024 · DOI：10.1016/j.eclinm.2024.102965 · 被引用次数：24 · 研究领域：Economic and Environmental Valuation、Health Systems, Economic Evaluations, Quality of Life、Decision-Making and Behavioral Economics

Background 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. Methods 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 I 2 statistic, and sources of heterogeneity using meta-regression. This study is registered with PROSPERO (CRD42023451545). Findings 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, I 2 = 97%) and 52% (95% CI:32–72, I 2 = 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 logi...