Department of Epidemiology, Boston University, Boston, MA, USA.
Department of Epidemiology, Boston University, Boston, MA, USA; Breast Oncology Center, Dana Farber Cancer Institute, Boston, MA, USA.
J Clin Epidemiol. 2022 Apr;144:127-135. doi: 10.1016/j.jclinepi.2021.12.028. Epub 2022 Jan 5.
Developing a causal graph is an important step in etiologic research planning and can be used to highlight data flaws and irreparable bias and confounding. As a case study, we consider recent findings that suggest human papillomavirus (HPV) vaccine is less effective against HPV-associated disease among girls living with HIV compared to girls without HIV.
To understand the relationship between HIV status and HPV vaccine effectiveness, it is important to outline the key assumptions of the causal mechanisms before designing a study to investigate the effect of the HPV vaccine in girls living with HIV infection.
We present a causal graph to describe our assumptions and proposed approach to explore this relationship. We hope to obtain feedback on our assumptions before data analysis and exemplify the process for designing causal graphs to inform an etiologic study.
The approach we lay out in this paper may be useful for other researchers who have an interest in using causal graphs to describe and assess assumptions in their own research before undergoing data collection and/or analysis.
在病因研究规划中，构建因果关系图是重要的一步，它可以突出数据缺陷以及不可弥补的偏差和混杂。作为一个案例研究，我们考虑了最近的发现，即在感染 HIV 的女孩中，HPV 疫苗对 HPV 相关疾病的有效性低于未感染 HIV 的女孩。
为了理解 HIV 状态与 HPV 疫苗有效性之间的关系，在设计研究来调查 HPV 疫苗在 HIV 感染女孩中的效果之前，概述因果机制的关键假设非常重要。
我们提出了一个因果关系图来描述我们的假设和拟议的方法来探索这种关系。我们希望在数据分析之前获得对我们假设的反馈，并举例说明设计因果关系图的过程，以告知病因研究。
本文中我们提出的方法可能对其他有兴趣在进行数据收集和/或分析之前，使用因果关系图来描述和评估自己研究中的假设的研究人员有用。
