Attribute Development in Health-Related Discrete Choice Experiments: A Systematic Review of Qualitative Methods and Techniques to Inform Quantitative Instruments

数据提取 数据收集 计算机科学 稳健性(进化) 数据科学 管理科学 系统回顾 批判性评价 定性研究 定性性质 数据质量 梅德林 医学 机器学习 替代医学 运营管理 统计 经济 政治学 社会科学 化学 法学 公制(单位) 数学 生物化学 病理 社会学 基因
作者
Natalia Gonzalez Bohorquez,Christina Malatzky,Steven McPhail,Remai Mitchell,Megumi Lim,Sanjeewa Kularatna
出处
期刊:Value in Health [Elsevier BV]
卷期号:27 (11): 1620-1633 被引量:15
标识
DOI:10.1016/j.jval.2024.05.014
摘要

OBJECTIVES: This review sought to identify the qualitative methods and techniques that researchers have used in the past decade to develop attributes and inform health-related discrete choice experiments (DCEs) surveys from a patient perspective. METHODS: The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines for reporting systematic reviews. An adapted appraisal tool following guidelines for reporting qualitative research for quantitative instruments and criteria for attribute development in DCEs was applied for quality assessment and data extraction. A narrative approach was used to synthesize data. This examination included consideration of issues pertaining to sampling, data collection, data analysis, attribute list reduction, wording, methodological adaptations to capture patient preferences, and testing the pre-experimental design decisions of the DCE survey. RESULTS: Of 8505 articles identified for abstract screening, 680 were included for full-text screening, 36 of which met the inclusion criteria. Practices to improve methodological robustness included pre-data collection materials to inform instruments, data collection methods specific for decision-making scenarios, purposeful selection of data analysis methods to address the research question, and participants' involvement in reducing the list of attributes. Examples of methodological adaptations for patients were noted. CONCLUSIONS: DCEs have the potential to become a mixed-method approach in which the qualitative phase informs a reduced list of attributes for a survey, serves the predesign decisions of the experiment by testing trade-offs, overlapping, understandability, face, and content validity and provides explanations of the quantitative results. Establishing guidelines for using qualitative methods for DCE attribute development may help to broadly enhance the methodological robustness of DCEs.
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