Shi Lizheng, Wu Eric Q, Hodges Meredith, Yu Andrew, Birnbaum Howard
Department of Health Systems Management, School of Public Health and Tropical Medicine, Tulane University, New Orleans, Louisiana 70112, USA.
Pharmacoeconomics. 2007;25(7):563-76. doi: 10.2165/00019053-200725070-00003.
Retrospective database analyses pose a series of methodological challenges, some of which are unique to their data sources, particularly in countries outside the US. This study aimed to qualitatively review the methodological challenges of using non-US databases to conduct retrospective economic and outcomes research studies. We conducted a MEDLINE search to obtain a sample of literature published after the year 2000 on retrospective analyses using non-US databases. We reviewed all relevant components of the selected articles in accordance with the checklist proposed for retrospective database studies by the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) Task Force and identified issues found in the data sources, methods, study designs, statistics and sources of possible threats to internal and external validity. We found a wide variation in the quality of studies in terms of outcome definitions, patient selection criteria, data collection methods, sample sizes, risk adjustment methods, potential measurement errors and external validity of the studies. Few economic studies included information on indirect cost components because of a lack of relevant data. The quality of non-US retrospective database analyses varied. Future such analyses may be improved if researchers implement the checklist developed by the ISPOR Task Force on Retrospective Database Studies.
回顾性数据库分析带来了一系列方法学挑战，其中一些挑战是其数据源所特有的，尤其是在美国以外的国家。本研究旨在定性评估使用非美国数据库进行回顾性经济和结果研究的方法学挑战。我们进行了MEDLINE检索，以获取2000年后发表的关于使用非美国数据库进行回顾性分析的文献样本。我们根据国际药物经济学和结果研究协会（ISPOR）特别工作组为回顾性数据库研究提出的清单，对所选文章的所有相关部分进行了审查，并确定了在数据源、方法、研究设计、统计学以及对内部和外部有效性可能构成威胁的来源中发现的问题。我们发现，在结果定义、患者选择标准、数据收集方法、样本量、风险调整方法、潜在测量误差和研究的外部有效性方面，研究质量存在很大差异。由于缺乏相关数据，很少有经济研究纳入间接成本组成部分的信息。非美国回顾性数据库分析的质量参差不齐。如果研究人员实施ISPOR回顾性数据库研究特别工作组制定的清单，未来此类分析可能会得到改进。
