Patients’ Preferences for Outcome, Process and Cost Attributes in Cancer Treatment: A Systematic Review of Discrete Choice Experiments

The Patient - Patient-Centered Outcomes Research(2017)

引用 59|浏览13
暂无评分
摘要
Introduction As several studies have been conducted to elicit patients’ preferences for cancer treatment, it is important to provide an overview and synthesis of these studies. This study aimed to systematically review discrete choice experiments (DCEs) about patients’ preferences for cancer treatment and assessed the relative importance of outcome, process and cost attributes. Methods A systematic literature review was conducted using PubMed and EMBASE to identify all DCEs investigating patients’ preferences for cancer treatment between January 2010 and April 2016. Data were extracted using a predefined extraction sheet, and a reporting quality assessment was applied to all studies. Attributes were classified into outcome, process and cost attributes, and their relative importance was assessed. Results A total of 28 DCEs were identified. More than half of the studies (56%) received an aggregate score lower than 4 on the PREFS (Purpose, Respondents, Explanation, Findings, Significance) 5-point scale. Most attributes were related to outcome (70%), followed by process (25%) and cost (5%). Outcome attributes were most often significant (81%), followed by process (73%) and cost (67%). The relative importance of outcome attributes was ranked highest in 82% of the cases where it was included, followed by cost (43%) and process (12%). Conclusion This systematic review suggests that attributes related to cancer treatment outcomes are the most important for patients. Process and cost attributes were less often included in studies but were still (but less) important to patients in most studies. Clinicians and decision makers should be aware that attribute importance might be influenced by level selection for that attribute.
更多
查看译文
关键词
Conjoint Analysis,Discrete Choice Experiment,Process Attribute,Attribute Identification,Outcome Attribute
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要