Cardiovascular Prevention and Pharmacotherapy

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Logistic Regression and Least Absolute Shrinkage and Selection Operator: Hyunyong Lee, Hun-Sung Kim; Cardiovasc Prev Pharmacother. 2020;2(4):142-146. Published online October 31, 2020; DOI: https://doi.org/10.36011/cpp.2020.2.e15

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Logistic regression, a model that forms a binary dependent variable and one or more independent variable(s), is used especially in epidemiological studies. By understanding the logistic model and its applications, such as odds ratio (OR) and performance efficiency, the concept of logistic regression can be easily grasped. The purpose of this article is to 1) introduce logistic regression, including odds and OR, 2) present predictive efficiency, such as area under the curve, and 3) explain the caution of logistic regression analysis.

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Perceptron: Basic Principles of Deep Neural Networks
Eung-Hee Kim, Hun-Sung Kim
Cardiovascular Prevention and Pharmacotherapy.2021; 3(3): 64. CrossRef

Causal Claims in Health Sciences and Medicine: a Difference-in-Differences Method: Kyoung-Nam Kim; Cardiovasc Prev Pharmacother. 2020;2(3):99-102. Published online July 31, 2020; DOI: https://doi.org/10.36011/cpp.2020.2.e13

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Abstract PDF: The difference-in-differences (DID) method is a useful tool to make causal claims using observational data. The key idea is to compare the difference between exposure and control groups before and after an event. The potential outcome of the exposure group during the post-exposure period is estimated by adding the observed outcome change of the control group between the pre- and post-exposure period to the observed outcome of the exposure group during the pre-exposure period. Because the effect of exposure is evaluated by comparing the observed outcome and potential outcome of the same exposure group, unmeasured potential confounders can be cancelled out by the design. To apply this method appropriately, the difference between the exposure and control groups needs to be relatively stable if no exposure occurred. Despite the strengths of the DID method, the assumptions, such as parallel trends and proper comparison groups, need to be carefully considered before application. If used properly, this method can be a useful tool for epidemiologists and clinicians to make causal claims with observational data.

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