BACKGROUND:Quasi-experimental methods (QEMs) are a family of techniques used to estimate causal relationships when randomized controlled trials are unfeasible or unethical. They offer a powerful alternative to observational studies by introducing random assignment of individuals or groups into their design, thereby offering stronger means of establishing causation. The use of QEMs in cardiovascular research has not been systematically examined to determine steps toward improving and expanding their use.METHODS:We identified 4 main techniques using a systematic search strategy from 2016 to 2021: instrumental variable analysis, interrupted time series analysis, difference-in-differences analysis, and regression discontinuity designs. QEMs are examined as alternatives to randomized controlled trials and traditional observational studies; as more observational data becomes available to researchers, there are more opportunities to apply these techniques. Eligible articles were selected based on publication in high-ranked journals. The quality of eligible articles was appraised using the Joanna Briggs Institute checklist for quasi-experimental studies.RESULTS:Data from 380 studies were extracted based on our inclusion criteria. Forty-two of these studies were published in the top 10 medical or top 20 cardiovascular disease journals, and 25 studies were included after quality appraisal. The review identifies the main features and limitations associated with each technique, providing readers with practical guidance on how to apply these to their research. A graphical decision aid was developed to facilitate the routine use of QEMs.CONCLUSIONS:The use of QEMs in cardiovascular research published in contemporary, high-impact articles was examined. Findings are biased toward this segment of literature, which represents the latest developments in this growing area of cardiovascular research. The decision aid is a novel schematic that researchers can adopt into practice.

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如何在心血管研究中使用准实验方法：当前实践回顾

背景：准实验方法（QEM）是一系列技术，用于在随机对照试验不可行或不道德时估计因果关系。它们通过在设计中引入个人或群体的随机分配，为观察性研究提供了一种强有力的替代方案，从而提供了更强有力的建立因果关系的方法。QEM 在心血管研究中的使用尚未经过系统检查，以确定改进和扩大其使用的步骤。方法：我们使用 2016 年至 2021 年的系统搜索策略确定了 4 种主要技术：工具变量分析、间断时间序列分析、差异分析无差异分析和断点回归设计。QEM 被视为随机对照试验和传统观察性研究的替代方案；随着研究人员获得更多的观测数据，应用这些技术的机会也越来越多。符合条件的文章是根据在高排名期刊上发表的文章来选择的。使用 Joanna Briggs Institute 准实验研究清单对合格文章的质量进行了评估。 结果：根据我们的纳入标准提取了 380 项研究的数据。其中 42 项研究发表在排名前 10 的医学或排名前 20 的心血管疾病期刊上，25 项研究经过质量评估后被纳入。该评论确定了与每种技术相关的主要特征和局限性，为读者提供了如何将这些技术应用到他们的研究中的实用指导。开发了图形决策辅助工具以促进 QEM 的常规使用。结论：对当代高影响力文章中发表的心血管研究中 QEM 的使用进行了检查。研究结果偏向于这部分文献，它们代表了心血管研究这一不断发展的领域的最新进展。该决策辅助是一种新颖的原理图，研究人员可以将其应用于实践。