Changes

* 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 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 consequence of the outcome of interest? If so, it cannot be considered a confounder.

* 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 by the confounding variable. Assume we have three binary variables - x, y and z. If variable y is completely confounding the relationship between variables x and z, then by calculating the odds ratio for the association between x and z at each level of y, the effect of y will be removed, and the odds ratio will be in the region of 1.0. It should be emphasised that no statistical tests for confounding are available, and so its presence requires careful logical consideration as well as investigations such as that described here.

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 by the confounding variable. As confounding variables have a differential distribution amongst exposed compared to unexposed groups, and amongst diseased compared to non-diseased groups, stratification according to them will remove this effect. Assume we have three binary variables - x, y and z. If variable y is completely confounding the relationship between variables x and z, then by calculating the odds ratio for the association between x and z at each level of y, the effect of y will be removed, and the odds ratio will be in the region of 1.0. It should be emphasised that no statistical tests for confounding are available, and so its presence requires careful logical consideration as well as investigations such as that described here.

==Dealing with confounding==

==Dealing with confounding==

Although the presence of confounding is a characteristic of the data, it needs to be accounted for in order to develop conclusions regarding true risk factors for the outcome of interest. One method which will be obvious based on the previous example is that of stratification - either during data collection or during data analysis.

Although the presence of confounding is a characteristic of the data, and is not an error as such, it can result in errors of interpretation of an analytic study if it is not accounted for. Methods available for accounting for confounding can be applied during the design of the study and selection of participants, or during analysis.

 

 

 

 

 

[[Category:Veterinary Epidemiology - General Concepts|J]]

[[Category:Veterinary Epidemiology - General Concepts|J]]