Cardiovascular Prevention and Pharmacotherapy

Special Articles

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.

Basic Concepts of a Mendelian Randomization Approach: Tae-Hwa Go, Dae Ryong Kang; Cardiovasc Prev Pharmacother. 2020;2(1):24-30. Published online January 31, 2020; DOI: https://doi.org/10.36011/cpp.2020.2.e3

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Abstract PDF: The Mendelian Randomization (MR) approach is a method that enables causal inference in observational studies. There are 3 assumptions that must be satisfied to obtain suitable results: 1) The genetic variant is strongly associated with the exposure, 2) The genetic variant is independent of the outcome, given the exposure and all confounders (measured and unmeasured) of the exposure-outcome association, 3) The genetic variant is independent of factors (measured and unmeasured) that confound the exposure-outcome relationship. This analysis has been used increasingly since 2011, but many researchers still do not know how to perform MR. Here, we introduce the basic concepts, assumptions, and methods of MR analysis to enable better understanding of this approach.

Improving Causal Inference in Observational Studies: Interrupted Time Series Design: Kyoung-Nam Kim; Cardiovasc Prev Pharmacother. 2020;2(1):18-23. Published online January 31, 2020; DOI: https://doi.org/10.36011/cpp.2020.2.e2

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Abstract PDF: Interrupted time series analysis is often used to evaluate the effects of healthcare policies and interventional projects using observational data. Interrupted time series analysis is one of the epidemiological methods, which are based on the assumption that the trend of the pre-intervention time series, if not intervened, would have the same tendency in the post-intervention period. Time series during the pre-intervention period are used to model a counterfactual situation without intervention during the post-intervention period. The effects of intervention can be seen in the form of abrupt changes in the result level (intercept) due to the intervention and/or changes in the result over time (slope) after the intervention. If the effects of intervention are predefined, the effects of the intervention can be distinguished and analyzed based on the time series analysis model constructed accordingly. Interrupted time series analysis is generally performed in a pre-post comparison using the intervention series. Recently, however, controlled interrupted time series analysis, which uses a control series as well as an intervention series, has also been used. The controlled interrupted time series analysis uses a control series to control potential confounding due to events occurring concurrently with the intervention of interest. Even though interrupted time series analysis is a useful way to assess the effects of intervention using observational data, misleading results can be derived if the conditions for proper application are not met. Before applying the method, it is necessary to make sure that the data conforms to the conditions for proper application.

Improving Causal Inference in Observational Studies: Propensity Score Matching: Min Heui Yu, Dae Ryong Kang; Cardiovasc Prev Pharmacother. 2019;1(2):57-62. Published online October 31, 2019; DOI: https://doi.org/10.36011/cpp.2019.1.e6

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Propensity score matching (PSM) is a useful statistical methods to improve causal inference in observational studies. It guarantees comparability between 2 comparison groups are required. PSM is based on a “counterfactual” framework, where a causal effect on study participants (factual) and assumed participants (counterfactual) are compared. All participants are divided into 2 groups with the same covariates matched as much as possible. Propensity score is used for matching, and it reflects the conditional probabilities that individuals will be included in the experimental group when covariates are controlled for all subjects. The counterfactuals for the experimental group are matched between groups with characteristics as similar as possible. In this article, we introduce the concept of PSM, PSM methods, limitations, and statistical tools.

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Strengthening Association through Causal Inference
Megan Lane, Nicholas L. Berlin, Kevin C. Chung, Jennifer F. Waljee
Plastic & Reconstructive Surgery.2023; 152(4): 899. CrossRef

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