In research, it is important to distinguish confounders from effect modifiers, as they require different analytical strategies to avoid bias and obtain accurate results. In the analysis, confounding factors must be adjusted to ensure that the observed effect is indeed due to the independent variable, while effect modifiers should be examined to understand how they affect the effect of the independent variable.

Confounders and effect(-measure) modifiers (EMM) are concepts in causal inference, which means they are not relevant for research questions outside this domain (risk factor analysis and prediction). And always remember that doing confounding or EMM analysis is decided based on our research question.

In causal questions, there are at least three ways to define "confounding": classical definition (a factor that causes both the exposure and outcome), collapsibility definition (differences in measure of association between unadjusted and adjusted analyses), and counterfactual definition (exchangeability) [1]. There are also several ways to define "effect(-measure) modifications": heterogeneity in terms of exposure-outcome effect between strata of a third variable, or presence of statistical interaction. There are a heavy body of literatures discussing these definitions as well as the pitfalls of each. Also note that in addition to EMM, there's also causal interaction, which explores the interaction of two main exposures at the same time [2].

Depending on how one defines confounding and EMM, the data analysis and presentation plans are different. For example:

Confounding:

• If you use the collapsibility definition, you create several univariate and multivariable models and compare the measures of association (PR, RR, OR, HR, etc.) of the main exposure across the models.
• If you use the counterfactual definition, you might want to start with a directed acyclic graph (DAG) and might as well try a causal inference method, such as propensity score methods.
• And you don't need to adjust for confounding if your study is a randomized trial.

EMM: you can do

• a stratified analysis,
• add a lot of interaction terms in your model, or
• use a DAG to select variables in the model before adding interaction terms.

Regarding EMM, it's worth noting that EMM depends on scale (additive or multiplicative). For example, EMM may exist on the additive scale but not exist on the multiplicative scale (and vice versa), or may be "positive" on a scale but "negative" on the other (e.g., superadditive but submultiplicative). Therefore, choosing the scale in EMM analysis is pretty important. Also note that if confounding exists, heterogeneity between strata will exist in at least one of the scales [3,4].

We often present only the effect of main exposure in both confounding and EMM analyses. But for EMM, we need to present the effects by stratum, or present the effect of interaction (for causal interaction analysis).

References

1. Greenland S, Robins JM, Pearl J. Confounding and Collapsibility in Causal Inference. Statistical Science. 1999;14(1):29–46. http://www.jstor.org/stable/2676645

2. VanderWeele TJ. On the distinction between interaction and effect modification. Epidemiology. 2009;20(6):863-871. doi:10.1097/EDE.0b013e3181ba333c

3. Vander Weele TJ. Confounding and effect modification: distribution and measure. Epidemiol Methods. 2012;1(1):55-82. doi:10.1515/2161-962X.1004

4. Webster-Clark M, Breskin A. Directed Acyclic Graphs, Effect Measure Modification, and Generalizability. Am J Epidemiol. 2021;190(2):322-327. doi:10.1093/aje/kwaa185

Yes, that is correct. When building a statistical model, assessing potential confounding and effect modification is important to ensure that the relationships between the independent and dependent variables are accurately estimated.

A confounding variable is a variable that is related to both the independent and dependent variables, which can lead to biased estimates of the association between them. To address confounding, researchers may adjust for confounding variables in the model or conduct sensitivity analyses to see how robust the findings are to potential confounding.

On the other hand, an effect modifier is a variable that changes the strength or direction of the relationship between the independent and dependent variables. In the presence of an effect modifier, the relationship between the independent and dependent variables differs across subgroups defined by the modifier variable. In this case, researchers may conduct subgroup analyses and present the results stratified by the effect modifier variable.

A literature review can provide valuable information about potential confounding and effect modification. If confounding or effect modification is identified, adjusting the model or presenting the results is important. For example, if there is confounding, the results should be presented with the adjusted estimates and confidence intervals. If there is effect modification, the results should be presented with subgroup analyses and interaction terms.

Le domaine épidémiologique est celui qui manipule le plus les statistiques (Toutes méthodes confondus). On doit prendre en considération l'ensemble des variables existantes dans le protocole d'observation.

Les analyses sont tributaires de ces dernières.

Les mesures des variables doivent à mon avis tenir compte des conditions de la station où sont repérés les paramètres naturels (à décrire rigoureusement). Bonne chance!