Tran Nguyen K, Lash Timothy L, Goldstein Neal D
Department of Epidemiology and Biostatistics, Dornsife School of Public Health, Drexel University, Philadelphia, PA, USA.
Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA.
Glob Epidemiol. 2021 Nov;3. doi: 10.1016/j.gloepi.2021.100066. Epub 2021 Nov 19.
As an inherent part of epidemiologic research, practical decisions made during data collection and analysis have the potential to impact the measurement of disease occurrence as well as statistical and causal inference from the results. However, the computational skills needed to collect, manipulate, and evaluate data have not always been a focus of educational programs, and the increasing interest in "data science" suggest that data literacy has become paramount to ensure valid estimation. In this article, we first motivate such practical concerns for the modern epidemiology student, particularly as it relates to challenges in causal inference; second, we discuss how such concerns may be manifested in typical epidemiological analyses and identify the potential for bias; third, we present a case study that exemplifies the entire process; and finally, we draw attention to resources that can help epidemiology students connect the theoretical underpinning of the science to the practical considerations as described herein.
作为流行病学研究的一个固有部分，在数据收集和分析过程中做出的实际决策有可能影响疾病发生率的测量以及结果的统计和因果推断。然而，收集、处理和评估数据所需的计算技能并非一直是教育项目的重点，而对“数据科学”日益增长的兴趣表明，数据素养已成为确保有效估计的关键。在本文中，我们首先激发现代流行病学学生对这类实际问题的关注，特别是与因果推断中的挑战相关的问题；其次，我们讨论这些问题在典型的流行病学分析中可能如何表现，并识别偏差的可能性；第三，我们展示一个案例研究，以例证整个过程；最后，我们提请注意一些资源，这些资源可以帮助流行病学学生将该学科的理论基础与本文所述的实际考量联系起来。
