Confounding - Revision history

Gauche at 17:39, 17 December 2010

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Gauche at 17:38, 17 December 2010

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Gauche at 17:37, 17 December 2010

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Gauche at 14:48, 17 December 2010

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Gauche at 14:47, 17 December 2010

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Gauche at 13:37, 17 December 2010

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Gauche at 13:08, 17 December 2010

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← Older revision		Revision as of 17:39, 17 December 2010
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−	The issue of confounding is of central importance in any analytic epidemiological study (as well as in ~~some~~ descriptive studies aiming to compare different populations), especially in the case of [[Study design\|observational studies]]. Confounding results from non-random differences between the groups of animals being compared in relation to a second, 'confounding' exposure which is independently associated with both the exposure of interest (although not a consequence of this) and the outcome of interest (although not an effect of this). This results in the effect of the exposure of interest is 'mixed up' with the effect of the confounding exposure, and therefore an incorrect estimate of the true association. As such, confounding is viewed by many authors as a form of bias - however, unlike forms of selection and information bias, it is a natural feature of the data (in the case of an observational study), and techniques are available to account for it during analysis.<br>	+	The issue of confounding is of central importance in any analytic epidemiological study (as well as in those descriptive studies aiming to compare different populations), especially in the case of [[Study design\|observational studies]]. Confounding results from non-random differences between the groups of animals being compared in relation to a second, 'confounding' exposure which is independently associated with both the exposure of interest (although not a consequence of this) and the outcome of interest (although not an effect of this). This results in the effect of the exposure of interest is 'mixed up' with the effect of the confounding exposure, and therefore an incorrect estimate of the true association. As such, confounding is viewed by many authors as a form of bias - however, unlike forms of selection and information bias, it is a natural feature of the data (in the case of an observational study), and techniques are available to account for it during analysis.<br>

	==An example of confounding==		==An example of confounding==

← Older revision		Revision as of 17:38, 17 December 2010
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−	The issue of confounding is of central importance in any analytic epidemiological study, especially in the case of [[Study design\|observational studies]]. Confounding results from non-random differences between the groups of animals being compared in relation to a second, 'confounding' exposure which is independently associated with both the exposure of interest (although not a consequence of this) and the outcome of interest (although not an effect of this). This results in the effect of the exposure of interest is 'mixed up' with the effect of the confounding exposure, and therefore an incorrect estimate of the true association. As such, confounding is viewed by many authors as a form of bias - however, unlike forms of selection and information bias, it is a natural feature of the data (in the case of an observational study), and techniques are available to account for it during analysis.<br>	+	The issue of confounding is of central importance in any analytic epidemiological study (as well as in some descriptive studies aiming to compare different populations), especially in the case of [[Study design\|observational studies]]. Confounding results from non-random differences between the groups of animals being compared in relation to a second, 'confounding' exposure which is independently associated with both the exposure of interest (although not a consequence of this) and the outcome of interest (although not an effect of this). This results in the effect of the exposure of interest is 'mixed up' with the effect of the confounding exposure, and therefore an incorrect estimate of the true association. As such, confounding is viewed by many authors as a form of bias - however, unlike forms of selection and information bias, it is a natural feature of the data (in the case of an observational study), and techniques are available to account for it during analysis.<br>

	==An example of confounding==		==An example of confounding==

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 ====Matching====
-This process involves ensuring that the level of exposure to the confounding variable is the same in the groups being compared, and may be performed on an individual level (matching each animal with at least one other of the same exposure to the confounder - known as ''individual matching'') or on a group level (attempting to make the distribution of exposure to the confounding variable similar in the two groups - known as ''frequency matching''). The effects of matching, and therefore the reasons for using a matching strategy may differ between different study designs, and may or may not be primarily aimed at reduction of confounding. Care also needs to be taken when using matching, as there is a risk of '''overmatching''' (especially in case-control studies), where the true effect of the variable of interest is unable to be measured due to a close association with the matching variable.
+This process involves ensuring that the level of exposure to the confounding variable is the same in the groups being compared, and may be performed on an individual level (matching each animal with at least one other of the same exposure to the confounder - known as ''individual matching'', or ''pair matching'') or on a group level (attempting to make the distribution of exposure to the confounding variable similar in the two groups - known as ''frequency matching''). The effects of matching, and therefore the reasons for using a matching strategy may differ between different study designs, and may or may not be primarily aimed at reduction of confounding. Care also needs to be taken when using matching, as there is a risk of '''overmatching''' (especially in case-control studies), where the true effect of the variable of interest is unable to be measured due to a close association with the matching variable. It is also important to note that in the case of individually matched studies, or when matching is used in case-control studies, this matching must be accounted for in the analysis.
 ===Techniques applied during study analysis===
 ====Stratification====
 ====Standardisation====
 ====Multivariable techniques====
 [[Category:Veterinary Epidemiology - General Concepts|J]]

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 ===Techniques applied during study design===
 ====Restriction====
 ====Matching====
 ===Techniques applied during study analysis===

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 ====Standardisation====
-====Multivariable models====
+====Multivariable techniques====
 [[Category:Veterinary Epidemiology - General Concepts|J]]

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 * 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.
-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==
-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]]

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 ==Identifying counfounding==
 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.
 ==Dealing with confounding==

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 The issue of confounding is of central importance in any analytic epidemiological study, especially in the case of [[Study design|observational studies]]. Confounding results from non-random differences between the groups of animals being compared in relation to a second, 'confounding' exposure which is independently associated with both the exposure of interest (although not a consequence of this) and the outcome of interest (although not an effect of this). This results in the effect of the exposure of interest is 'mixed up' with the effect of the confounding exposure, and therefore an incorrect estimate of the true association. As such, confounding is viewed by many authors as a form of bias - however, unlike forms of selection and information bias, it is a natural feature of the data (in the case of an observational study), and techniques are available to account for it during analysis.<br>
-As the concept of confounding can be difficult to understand, an example is given here. Consider a cross sectional study investigating the association between the use of (moderately effective) anthelmintics and infection with ''Echinococcus granulosus'' tapeworm in a sheep farming area where the parasite is known to exist. Initial results show that animals which were not dosed recently with anthelmintics were more likely to be infected with the tapeworm; however, further investigation shows that these dogs were also more likely to have free access to roam on the surrounding fields. In this example, free roaming is ''confounding'' the association between anthelmintic use and infection: dogs which are allowed to roam freely are both more likely to not have been dosed with anthelmintics (because their owners have more trouble restraining them for dosing) and are more likely to acquire infection (from infected dead sheep they find whilst roaming). Roaming freely is not a consequence of failure of having been dosed with anthelmintics (i.e. it does not lie on the ''causal pathway'' between anthelmintic use and infection), nor is it a consequence of having become infected with the tapeworm. If the effect of roaming was removed, the expected association between infection and recent use of anthelmintics would be expected to be reduced (although it would still be expected to be present to some degree). A common example of confounding used in human epidemiology is the association between alcohol consumption and lung cancer, which is confounded by cigarette smoking (i.e. people who drink more are more likely to also smoke cigarettes, which are also known to be associated with development of lung cancer).
+==An example of confounding==
 [[Category:Veterinary Epidemiology - General Concepts|J]]