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Similar considerations arise when taking repeated measurements of some physical characteristic such as the electrical conductivity of copper.
This situation often arises when we seek knowledge about the cause system of which the observed population is an outcome.
We could also stratify by these factors to see if they were confounders and to adjust for them.
A stratified analysis is easy to do and gives you a fairly good picture of what's going on.
The general format is depicted here: is the total person-time in each stratum.
In the examples above we used just two levels or sub-strata or of the confounding variable, but one can use more than two sub-strata.
However, when we stratified the analysis into those age To explore and adjust for confounding, we can use a stratified analysis in which we set up a series of two-by-two tables, one for each stratum (category) of the confounding variable.
The weighted averages for risk ratios and odds ratios are computed as follows: stratum.
However, a major disadvantage to stratification is its inability to control simultaneously for multiple confounding variables.
For example, you might decide to control for gender, 3 levels of smoking exposure, 4 levels of age, and 4 levels of BMI.
This is particularly important when using stratification to control for confounding by a continuously distributed variable like age.
In the example above looking at the relationship between obesity and CVD we stratified the analysis by age, looking at the relationship in subjects .