Grubic Nicholas, Johnston Amy, Randhawa Varinder K, Humphries Karin H, Rosella Laura C, Maximova Katerina
Division of Epidemiology, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.
Department of Obstetrics and Gynecology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada. Electronic address: https://twitter.com/Johnston.
Can J Cardiol. 2025 May;41(5):996-1009. doi: 10.1016/j.cjca.2024.12.022. Epub 2024 Dec 19.
Systematic error, often referred to as bias is an inherent challenge in observational cardiovascular research, and has the potential to profoundly influence the design, conduct, and interpretation of study results. If not carefully considered and managed, bias can lead to spurious results, which can misinform clinical practice or public health initiatives and compromise patient outcomes. This methodological primer offers a concise introduction to identifying, evaluating, and mitigating bias in observational cardiovascular research studies that examine the causal association between an exposure (or treatment) and an outcome. Using high-profile examples from the cardiovascular literature, this review provides a theoretical overview of 3 main types of bias-selection bias, information bias, and confounding-and discusses the implications of specialized types of biases commonly encountered in longitudinal cardiovascular research studies, namely, competing risks, immortal time bias, and confounding by indication. Furthermore, strategies and tools that can be used to minimize and assess the influence of bias are highlighted, with a specific focus on using the target trial framework, directed acyclic graphs, quantitative bias analysis, and formal risk of bias assessments. This review aims to assist researchers and health care professionals in designing observational studies and selecting appropriate methodologies to reduce bias, ultimately enhancing the estimation of causal associations in cardiovascular research.
系统误差，通常被称为偏倚，是观察性心血管研究中固有的挑战，并且有可能深刻影响研究结果的设计、实施及解读。如果不仔细考虑和处理，偏倚可能导致虚假结果，进而误导临床实践或公共卫生举措，并危及患者预后。本方法学入门读物简要介绍了在观察性心血管研究中识别、评估和减轻偏倚的方法，这些研究旨在探究暴露（或治疗）与结局之间的因果关联。本文通过心血管文献中的典型实例，对三种主要类型的偏倚——选择偏倚、信息偏倚和混杂偏倚——进行了理论概述，并讨论了纵向心血管研究中常见的特殊类型偏倚的影响，即竞争风险、不朽时间偏倚和指征性混杂。此外，本文还重点介绍了可用于最小化和评估偏倚影响的策略与工具，特别关注使用目标试验框架、有向无环图、定量偏倚分析以及正式的偏倚风险评估。本综述旨在帮助研究人员和医疗保健专业人员设计观察性研究并选择合适的方法以减少偏倚，最终提高心血管研究中因果关联的估计准确性。
