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Practical data considerations for the modern epidemiology student
Journal article   Open access   Peer reviewed

Practical data considerations for the modern epidemiology student

Nguyen K. Tran, Timothy L. Lash and Neal D. Goldstein
Global Epidemiology, v 3, p100066
01 Nov 2021
DOI: https://doi.org/10.1016/j.gloepi.2021.100066
PMID: 35844206
url
https://doi.org/10.1016/j.gloepi.2021.100066View
Published, Version of Record (VoR) Open

Abstract

Biostatistics Causal inference Data science Education and training Epidemiology
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.

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