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====Matching====
====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===
===Techniques applied during study analysis===
====Stratification====
====Stratification====
As described earlier, stratification is one method of identification of possible confounding. It can also be used in the case of descriptive studies which may be affected by confounders - for example, if an attempt was made to compare mortality rates amongst two groups with different age structures. Of course, presenting two measures of association (one for each level of the confounding variable) can make clear interpretation of associations more difficult. As such, statistical methods (such as the ''Wald test for homogeneity'') are available to assess whether the stratum-specific odds ratios are approximately equal. If so, a pooled odds ratio can be created, which is a single measure of association, accounting for the confounding variable. If the odds ratios are not equal, this may suggest the presence of '''interaction''' (also known as '''effect modification''').
====Standardisation====
====Standardisation====
Methods of standardisation can be used to account for confounders in both analytic studies and descriptive studies. Broadly, speaking, these methods involve the use of a ''standard population'' in order to remove the effect of differences in distribution of confounding variables between populations. Two methods of standardisation are recognised:
* Direct standardisation involves the weighting of the observed measures of disease frequency in the study population within each stratum of the confounding variable according to the distribution of these strata within the standard population, and should only be used in the case of large sample sizes. Therefore, this technique requires knowledge of both the stratum-specific estimates in the study population and the distribution of strata of the confounder in the standard population.
* Indirect standardisation involves the comparison of the observed disease frequency with that which would be expected if the stratum-specific measures of frequency were the same as those in the standard population, and can be used with smaller sample sizes. As such, it requires knowledge of the stratum-specific estimates of disease frequency in the standard population and the distribution of strata of the confounding variable in the study population. Techniques are used in order to calculate a '''standardised mortality (or morbidity) ratio''', which is a measure of how many times more common the disease is in the study population than in the standard population.
====Multivariable techniques====
====Multivariable techniques====
Multivariable techniques such as linear regression and logistic regression allow a number of variables to be accounted for simultaneously, and allow easy interpretation of the effect of each of these whilst the others are held at a set level. These techniques are the most commonly used method of accounting for confounding, and also allow a number of different exposures to be investigated simultaneously.
[[Category:Veterinary Epidemiology - General Concepts|J]]
[[Category:Veterinary Epidemiology - General Concepts|J]]