Rao Naman R, More Chandramani B, Brahmbhatt Rahi M, Chen Youbai, Ming Wai-Kit
Harvard Medical School, Harvard University, Boston, MA, USA.
Department of Oral Medicine and Radiology, K. M. Shah Dental College and Hospital, Sumandeep Vidyapeeth University, Vadodara, Gujarat, India.
J Oral Biol Craniofac Res. 2020 Oct-Dec;10(4):356-360. doi: 10.1016/j.jobcr.2020.06.008. Epub 2020 Jul 3.
Oral submucous fibrosis (OSMF), although already established as an oral potentially malignant disorder (OPMD), still stands over a weak bridge because of its controversial pathogenesis. There has been tremendous work on this disease since 1962, surprisingly, we are unsuccessful in finding the exact causation of OSMF. The potential cause for this is either a lack of systematically performed clinical observational studies or over-interpreted inferences of the presented results. Accordingly, the literature is piled with complex data that is being followed by emerging researchers. Hence, this conceptual paper is presented to focus and explain only the epidemiological concepts of causal inference and the construction of DAGs. These concepts will help to encode our subject matter knowledge and assumptions regarding the causal structure problem, classify the source of systematic bias, identify the potential confounders, potential issues in the study design, and guide the data analysis.
口腔黏膜下纤维化（OSMF）虽然已被确认为一种口腔潜在恶性疾病（OPMD），但由于其发病机制存在争议，目前仍处于薄弱环节。自1962年以来，针对这种疾病开展了大量研究，令人惊讶的是，我们仍未找到OSMF的确切病因。造成这种情况的潜在原因要么是缺乏系统开展的临床观察研究，要么是对所呈现结果的推断过度解读。因此，文献中充斥着复杂的数据，新兴研究人员正遵循这些数据。因此，本文提出这一概念性文章，仅聚焦并解释因果推断的流行病学概念以及有向无环图（DAGs）的构建。这些概念将有助于对我们关于因果结构问题主题知识和假设进行编码，对系统偏差来源进行分类，识别潜在混杂因素、研究设计中的潜在问题，并指导数据分析。
