Question

In multivariable analysis, the terms confounding and effect modification come into play. Why do you think they are important? What effect do they have in statistical analysis?

Answer 1

The terms Confounding and Effect modification come into play, in multivariate analysis.

They are important because :-

a.) Confounding :-  If other factors that influence the outcome are unevenly distributed between the groups, these other factors can distort the apparent association between the outcome and the primary exposure of interest; this is what is meant by confounding. When there is confounding, multi-variable methods can be used to estimate the association between an exposure and an outcome after adjusting for, or taking into account, the impact of one or more confounding factors (other risk factors). In essence, multiple variable analysis allows us to assess the independent effect of each of the exposures.

b.) Effect Modification :- They occurs when the magnitude of the effect of the primary exposure on an outcome (i.e., the association) differs depending on the level of a third variable. For example, suppose a clinical trial is conducted and the drug is shown to result in a statistically significant reduction in total cholesterol. However, suppose that with closer scrutiny of the data, the investigators find that the drug is only effective in subjects with a specific genetic marker and that there is no effect in persons who do not possess the marker. This is an example of effect modification or "interaction". The effect of the treatment is different depending on the presence or absence of the genetic marker. Multi-variable methods can also be used to assess effect modification. This phenomenon of "effect modification" is distinct from confounding. When effect modification is present, it would be misleading to compute an overall estimate of the association because the association is different for those with or without the third factor. A common way of dealing with effect modification is examine the association separately for each level of the third variable. Nevertheless, multiple variable procedures can be used to identify effect modification.

The effects they have in statistical analysis are:-

a.) Confounding :-

Confounding is a distortion (inaccuracy) in the estimated measure of association that occurs when the primary exposure of interest is mixed up with some other factor that is associated with the outcome.

There are three conditions that must be present for confounding to occur:

1. The confounding factor must be associated with both the risk factor of interest and the outcome.
2. The confounding factor must be distributed unequally among the groups being compared.
3. A confounder cannot be an intermediary step in the causal pathway from the exposure of interest to the outcome of interest.

Since most health outcomes have multiple contributing causes, there are many possible confounders. For example, a study looking at the association between obesity and heart disease might be confounded by age, diet, smoking status, and a variety of other risk factors that might be unevenly distributed between the groups being compared.

b.)  Effect Modification :-

Effect modification occurs when the magnitude of the effect of the primary exposure on an outcome (i.e., the association) differs depending on the level of a third variable. In this situation, computing an overall estimate of association is misleading. One common way of dealing with effect modification is examine the association separately for each level of the third variable. For example, suppose a clinical trial is conducted and the drug is shown to result in a statistically significant reduction in total cholesterol. However, suppose that with closer scrutiny of the data, the investigators find that the drug is only effective in subjects with a specific genetic marker and that there is no effect in persons who do not possess the marker. The effect of the treatment is different depending on the presence or absence of the genetic marker. This is an example of effect modification or "interaction".

Unlike confounding, effect modification is a biological phenomenon in which the exposure has a different impact in different circumstances. Another good example is the effect of smoking on risk of lung cancer. Smoking and exposure to asbestos are both risk factors for lung cancer. Non-smokers exposed to asbestos have a 3-4 fold increased risk of lung cancer, and most studies suggest that smoking increases the risk of lung cancer about 20 times. However, shipyard workers who chronically inhaled asbestos fibers and also smoked had about a 64-fold increased risk of lung cancer. In other words, the effects of smoking and asbestos were not just additive – they were multiplicative. This suggests synergism or interaction, i.e., that the effect of smoking is somehow magnified in people who have also been exposed to asbestos. Multivariable methods can also be used to assess effect modification.