Question

How would you define the effects of confounding in respect to mortality calculations? What are these confounding variables and how do they affect mortality?

Answer 1

Lets say you compare mortality between two groups, one with heavy consumption of alcohol and the other with less consumption of alcohol. In this case alcohol consumption would be your independent variable and mortality would be your dependent variable.

If you find that people who consume more alcohol are more likely to die, it might seem intuitive to conclude that alcohol use increases the risk of death. In reality, it is possible that alcohol use is not the only mortality-affecting factor that differs between the two groups.

People who consume less alcohol might be more likely to eat a healthier diet or less likely to smoke, for example. Eating a healthy diet or smoking might in turn affect mortality. These other influencing factors are called confounding variables. If you ignore them and assume that any differences in mortality must be caused by a difference in alcohol consumption, you could end up with results that don’t reflect reality all that well. You might find associations where in reality there are none, or fail to find associations where they do in fact exist.

Conclusions

The association between two variables might be modified by a third variable, and this can lead to distorted results. Even after taking this into account in study design and data analysis your data could still be distorted by confounding – there might e.g. be other confounding factors you don’t know of – but the first steps in reducing its effects are being aware of its potential to distort your results and planning accordingly.

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