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Table 2 Fallacy in Descriptive Epidemiology: Bringing Machine Learning to the Table

Author

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  • Christoffer Dharma

    (Dalla Lana School of Public Health, University of Toronto, Toronton, ON M5T 3M7, Canada
    Center for Addictions and Mental Health, Toronto, ON M6J 1H4, Canada)

  • Rui Fu

    (Dalla Lana School of Public Health, University of Toronto, Toronton, ON M5T 3M7, Canada
    Department of Otolaryngology—Head and Neck Surgery, Temerty Faculty of Medicine, Sunnybrook Hospital, Toronto, ON M4N 3M5, Canada)

  • Michael Chaiton

    (Dalla Lana School of Public Health, University of Toronto, Toronton, ON M5T 3M7, Canada
    Center for Addictions and Mental Health, Toronto, ON M6J 1H4, Canada
    Ontario Tobacco Research Unit, Toronto, ON M5S 2S1, Canada)

Abstract

There is a lack of rigorous methodological development for descriptive epidemiology, where the goal is to describe and identify the most important associations with an outcome given a large set of potential predictors. This has often led to the Table 2 fallacy, where one presents the coefficient estimates for all covariates from a single multivariable regression model, which are often uninterpretable in a descriptive analysis. We argue that machine learning (ML) is a potential solution to this problem. We illustrate the power of ML with an example analysis identifying the most important predictors of alcohol abuse among sexual minority youth. The framework we propose for this analysis is as follows: (1) Identify a few ML methods for the analysis, (2) optimize the parameters using the whole data with a nested cross-validation approach, (3) rank the variables using variable importance scores, (4) present partial dependence plots (PDP) to illustrate the association between the important variables and the outcome, (5) and identify the strength of the interaction terms using the PDPs. We discuss the potential strengths and weaknesses of using ML methods for descriptive analysis and future directions for research. R codes to reproduce these analyses are provided, which we invite other researchers to use.

Suggested Citation

  • Christoffer Dharma & Rui Fu & Michael Chaiton, 2023. "Table 2 Fallacy in Descriptive Epidemiology: Bringing Machine Learning to the Table," IJERPH, MDPI, vol. 20(13), pages 1-12, June.
  • Handle: RePEc:gam:jijerp:v:20:y:2023:i:13:p:6194-:d:1175932
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    References listed on IDEAS

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    1. van Buuren, Stef & Groothuis-Oudshoorn, Karin, 2011. "mice: Multivariate Imputation by Chained Equations in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i03).
    2. Zou, Hui, 2006. "The Adaptive Lasso and Its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1418-1429, December.
    3. Michael Chaiton & Iman Musani & Mari Pullman & Carmen H. Logie & Alex Abramovich & Daniel Grace & Robert Schwartz & Bruce Baskerville, 2021. "Access to Mental Health and Substance Use Resources for 2SLGBTQ+ Youth during the COVID-19 Pandemic," IJERPH, MDPI, vol. 18(21), pages 1-20, October.
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