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==Identifying counfounding==
==Identifying counfounding==
The first step in identifying confounding should be based on logical consideration of the suspected association:
The first step in identifying confounding should be based on logical consideration of the suspected association:
* is there a plausible confounding effect?
* is there a plausible confounding effect? That is, is the suspected confounding variable independently associated with both the exposure of interest and the outcome of interest?
* is the suspected confounding variable a direct consequence of the exposure of interest? For example, in the example given above, access to dead sheep could not be considered as a confounding variable for the association between roaming and infection, as it is the main presumed mechanism of action of roaming. That is, dogs which roam more have greater access to dead sheep, and dogs which are not allowed to roam would be expected to have minimal access to dead sheep.
* is the suspected confounding variable a consequence of the exposure of interest? If so, it cannot be considered a confounder. For example, in the example given above, access to dead sheep could not be considered as a confounding variable for the association between roaming and infection, as it is the main presumed mechanism of action of roaming. That is, dogs which roam more have greater access to dead sheep, and dogs which are not allowed to roam would be expected to have minimal access to dead sheep.
* is the suspected confounding variable a direct consequence of the outcome of interest?
* is the suspected confounding variable a consequence of the outcome of interest? If so, it cannot be considered a confounder.
If there is a logical explanation for the association, then the effect of the confounding variable on the measure of association (commonly, the odds ratio) between the exposure and outcome of interest should be investigated. There are a number of approaches for this available (commonly using multivariable techniques), but the basic principle can be illustrated by using the example of stratification of the data according to the confounding variable.
==Dealing with confounding==
==Dealing with confounding==