Multiple Sclerosis Risk Among Anti-tumor Necrosis Factor Alpha Users: A Methodological Review of Observational Studies Based on Real-world Data

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.

Keywords: Anti-TNFα; causal directed acyclic graphs; collider; confounding; mediation.; multiple sclerosis.