• Ryan Keen and Henning Tiemeier.
• Department of Social and Behavioral Sciences, Harvard University, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.
• World Neurosurg. 2022 May 1; 161: 245-250.

AbstractIt is essential for any epidemiologic and clinical investigation to determine the appropriate covariates for which to ascertain measures and subsequently model. A number of recent articles have sought to elucidate covariate selection in the context of data analysis. Unfortunately, few articles characterize covariate selection in the context of data collection and discuss their principles under the assumption that data are measured and available for analyses. Additionally, many articles delineating the appropriate principles use jargon that may be inaccessible to the audiences that need to understand them most. Considering these gaps, this paper first seeks to put forth a simple foundational guide to primary data collection by explaining four sets of covariates for which to ascertain measures: 1) all covariates that cause both the exposure and outcome; 2) selected covariates that cause the exposure; 3) selected covariates that cause the outcome; and 4) relevant sociodemographic and baseline covariates. To the extent possible, this paper attempts to communicate these principles clearly and in the absence of advanced causal inference terminology. Finally, this paper provides a conceptual framework for covariate inclusion and exclusion with respect to data analysis and regression modeling. Specifically, this framework suggests that regression models 1) include all known common cause covariates; 2) include all sociodemographic covariates; 3) exclude any covariate that is known to be both a consequence of the exposure and cause of the outcome; and 4) generally, for every term included in the statistical model, there should be at least 10 observations in the data set.Copyright © 2021 Elsevier Inc. All rights reserved.

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