Concepts (R)

Confounding

Confounding is a pervasive concern in epidemiology, especially in observational studies focusing on causality. Epidemiologists need to carefully select confounders to avoid biased results due to third factors affecting the relationship between exposure and outcome. Commonly used methods for selecting confounders, such as change-in-estimator or solely relying on p-value-based statistical methods, may be inadequate or even problematic.

Epidemiologists need a more formalized system for confounder selection, incorporating causal diagrams (Greenland, Pearl, and Robins 1999; Tennant et al. 2021) and counterfactual reasoning. This includes an understanding of the underlying causal relationships and the potential impacts of different variables on the observed association. Understanding the temporal order and causal pathways is crucial for accurate confounder control.

However, it is possible that epidemiologists may lack comprehensive knowledge about the causal roles of all variables and hence may need to resort to empirical criteria (VanderWeele 2019) such as the disjunctive cause criterion, or other variable selection methods such as machine learning approaches. While these methods can provide more sophisticated analyses and help address the high dimensionality and complex structures of modern epidemiological data, epidemiologists need to understand how these approaches function, along with their benefits and limitations, to avoid introducing additional bias into the analysis.

Effect modifier

Effect modification and interaction are two distinct concepts in epidemiology (VanderWeele 2009; Bours 2021). Effect modification occurs when the causal effect of an exposure (A) on an outcome (Y) varies based on the levels of a third factor (B).

In this scenario, the association between the exposure and the outcome differs within the strata of a second exposure, which acts as the effect modifier. For instance, the impact of alcohol (A) on oral cancer (Y) might differ based on tobacco smoking (B).

On the other hand, interaction refers to the joint causal effect of two exposures (A and B) on an outcome (Y). It examines how the combination of multiple exposures influences the outcome, such as the combined effect of alcohol (A) and tobacco smoking (B) on oral cancer (Y).

In essence, while effect modification looks at how a third factor influences the relationship between an exposure and an outcome, interaction focuses on the combined effect of two exposures on the outcome.

Table 2 fallacy

The “Table 2 Fallacy” in epidemiology refers to the misleading practice of presenting multiple adjusted effect estimates from a single statistical model in one table, often resulting in misinterpretation. This occurs when researchers report both the primary exposure’s effects and secondary exposures’ (often an adjustment variable for the primary exposure) effects without adequately distinguishing between the types of effects or considering the causal relationships among variables.

This idea highlights the potential for misunderstanding in interpreting the effects of various exposures on an outcome when they are reported together, leading to confusion over the nature and magnitude of the relationships and possibly influencing the design and interpretation of further studies (Westreich and Greenland 2013). The fallacy demonstrates the need for careful consideration of the types of effects estimated and reported in statistical models, urging researchers to be clear about the distinctions and implications of controlled direct effects, total effects, and the presence of confounding or mediating variables.

Reading list

Confounding key reference: (VanderWeele 2019; Tennant et al. 2021)

Effect modification key reference: (VanderWeele 2009; Bours 2021)

Table 2 fallacy key reference: (Westreich and Greenland 2013)

Optional reading:

Video Lessons

Potential outcome framework

What is included in this Video Lesson:

  • 0:00 Introduction
  • 0:16 Notations
  • 2:40 Treatment Effect
  • 6:13 Real-world Problem of the counterfactual definition
  • 9:44 Real-world Solution in Observational Setting

The timestamps are also included in the YouTube video description.

DAG

The video lesson split into 3 parts

DAG codes:

Example DAG codes can be accessed from this GitHub repository folder

Empirical criteria

When complete knowledge of DAG is unavailable

Modelling criteria for variable selection

Most of these modelling criteria work when we are only dealing with confounders (some important ones, and some less so), or maybe risk factors of the outcomes. But no mediators, or colliders.

Effect modification vs. interaction
Important

To revisit or deepen your grasp of these two concepts, consider reviewing this external tutorial.

Table 2 fallacy

Video Lesson Slides

Confounding

Effect modification

Table 2 fallacy

References

Bours, Martijn JL. 2021. “Tutorial: A Nontechnical Explanation of the Counterfactual Definition of Effect Modification and Interaction.” Journal of Clinical Epidemiology 134: 113–24.
Etminan, Mahyar, Gary S Collins, and Mohammad Ali Mansournia. 2020. “Using Causal Diagrams to Improve the Design and Interpretation of Medical Research.” Chest 158 (1): S21–28.
Greenland, Sander, Judea Pearl, and James M Robins. 1999. “Causal Diagrams for Epidemiologic Research.” Epidemiology, 37–48.
Heinze, Georg, Christine Wallisch, and Daniela Dunkler. 2018. “Variable Selection–a Review and Recommendations for the Practicing Statistician.” Biometrical Journal 60 (3): 431–49.
Lederer, David J, Scott C Bell, Richard D Branson, James D Chalmers, Rebecca Marshall, David M Maslove, Peter W Stewart, et al. 2019. “Control of Confounding and Reporting of Results in Causal Inference Studies: Guidance for Authors from Editors of Respiratory, Sleep, and Critical Care Journals.” Annals of the American Thoracic Society 16 (1): 22–28.
Tennant, Paul W, Elizabeth J Murray, Kathryn F Arnold, Leigh Berrie, Matthew P Fox, Samantha C Gadd, and George TH Ellison. 2021. “Use of Directed Acyclic Graphs (DAGs) to Identify Confounders in Applied Health Research: Review and Recommendations.” International Journal of Epidemiology 50 (2): 620–32.
VanderWeele, Tyler J. 2009. “On the Distinction Between Interaction and Effect Modification.” Epidemiology, 863–71.
———. 2019. “Principles of Confounder Selection.” European Journal of Epidemiology 34 (3): 211–19.
Westreich, Daniel, and Sander Greenland. 2013. “The Table 2 Fallacy: Presenting and Interpreting Confounder and Modifier Coefficients.” American Journal of Epidemiology 177 (4): 292–98.