Xing Xing, Wang Yipeng, Lin Lifeng
Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Department of Biostatistics, University of Florida, Gainesville, FL, USA.
J Clin Epidemiol. 2025 Mar;179:111645. doi: 10.1016/j.jclinepi.2024.111645. Epub 2024 Dec 18.
Trial sequential analysis (TSA) is an increasingly used tool in systematic reviews to monitor synthesized evidence. However, the current practice of TSAs often overlooks the order of same-year studies, which are typically ordered alphabetically based on the last names of the studies' authors by default in the widely used TSA software application. This practice is inappropriate and contrary to the TSA's definition. This issue is particularly concerning in systematic reviews on time-sensitive topics, such as COVID-19, where reviews include many studies within a short period. In this article, we use a case study to illustrate the impact of the order of same-year studies on TSA conclusions. It shows dramatically different patterns of evidence accumulation when same-year studies are ordered alphabetically vs in their actual temporal order. This article offers suggestions for authors to pay attention to study ordering in future TSAs.
序贯试验分析（TSA）是系统评价中用于监测综合证据的一种越来越常用的工具。然而，目前TSA的做法常常忽略同一年份研究的顺序，在广泛使用的TSA软件应用程序中，默认情况下，这些研究通常根据作者姓氏按字母顺序排列。这种做法是不合适的，并且与TSA的定义相悖。在对时间敏感的主题（如COVID-19）的系统评价中，这个问题尤其值得关注，因为这类评价在短时间内包含了许多研究。在本文中，我们通过一个案例研究来说明同一年份研究的顺序对TSA结论的影响。结果表明，当同一年份的研究按字母顺序排列与按实际时间顺序排列时，证据积累的模式有很大不同。本文为作者在未来的TSA中注意研究顺序提供了建议。
