AI Article Synopsis

  • Epidemiologic research has produced mixed findings regarding the link between anti-TNFα treatments and the risk of multiple sclerosis (MS) in patients with rheumatic or inflammatory bowel diseases.
  • Causal directed acyclic graphs (cDAGs) can help clarify these mixed results by visualizing potential biases, such as confounders and measurement errors, that might skew the data.
  • The article emphasizes the importance of understanding these biases to refine future research and improve the reliability of conclusions about the effects of anti-TNFα on MS risk.

Article Abstract

Epidemiologic studies on the risk of multiple sclerosis (MS) or demyelinating events associated with anti-tumor necrosis factor alpha (TNFα) use among patients with rheumatic diseases or inflammatory bowel diseases have shown conflicting results. Causal directed acyclic graphs (cDAGs) are useful tools for understanding the differing results and identifying the structure of potential contributing biases. Most of the available literature on cDAGs uses language that might be unfamiliar to clinicians. This article demonstrates how cDAGs can be used to determine whether there is a confounder, a mediator or collider-stratification bias and when to adjust for them appropriately. We also use a case study to show how to control for potential biases by drawing a cDAG depicting anti-TNFα use and its potential to contribute to MS onset. Finally, we describe potential biases that might have led to contradictory results in previous studies that examined the effect of anti-TNFα and MS, including confounding, confounding by contraindication, and bias due to measurement error. Clinicians and researchers should be cognizant of confounding, confounding by contraindication, and bias due to measurement error when reviewing future studies on the risk of MS or demyelinating events associated with anti-TNFα use. cDAGs are a useful tool for selecting variables and identifying the structure of different biases that can affect the validity of observational studies.

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Source
http://dx.doi.org/10.2174/1574886318666230726162245DOI Listing

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