What is stratification in confounding?

What is stratification in confounding?

Stratification allows to control for confounding by creating two or more categories or subgroups in which the confounding variable either does not vary or does not vary very much.

What does it mean for variables to be confounding?

Confounding means the distortion of the association between the independent and dependent variables because a third variable is independently associated with both. A causal relationship between two variables is often described as the way in which the independent variable affects the dependent variable.

How does stratification reduce confounding?

Stratification is an effective means for adjusting for confounding when the number of confounding factors is limited. Increasing the number of these factors will rapidly increase the number of strata, as the numbers of categories are multiplied.

What are confounders in epidemiology?

Confounding is one type of systematic error that can occur in epidemiologic studies. Confounding is the distortion of the association between an exposure and health outcome by an extraneous, third variable called a confounder.

What are examples of confounding variables?

For example, the use of placebos, or random assignment to groups. So you really can’t say for sure whether lack of exercise leads to weight gain. One confounding variable is how much people eat. It’s also possible that men eat more than women; this could also make sex a confounding variable.

How do you identify a confounding variable?

Identifying Confounding A simple, direct way to determine whether a given risk factor caused confounding is to compare the estimated measure of association before and after adjusting for confounding. In other words, compute the measure of association both before and after adjusting for a potential confounding factor.

Can confounding variables be controlled?

A Confounder is a variable whose presence affects the variables being studied so that the results do not reflect the actual relationship. There are various ways to exclude or control confounding variables including Randomization, Restriction and Matching.

How do you get rid of confounding variables?

There are several methods you can use to decrease the impact of confounding variables on your research: restriction, matching, statistical control and randomization. In restriction, you restrict your sample by only including certain subjects that have the same values of potential confounding variables.

Is gender a confounding variable?

Hence, due to the relation between age and gender, stratification by age resulted in an uneven distribution of gender among the exposure groups within age strata. As a result, gender is likely to be considered a confounding variable within strata of young and old subjects.

What is an example of confounding variables?

A confounding variable is an “extra” variable that you didn’t account for. They can ruin an experiment and give you useless results. For example, if you are researching whether lack of exercise leads to weight gain, then lack of exercise is your independent variable and weight gain is your dependent variable.

Is age a confounding variable?

Age is a confounding factor because it is associated with the exposure (meaning that older people are more likely to be inactive), and it is also associated with the outcome (because older people are at greater risk of developing heart disease).

When to use stratified analysis to test confounding?

This approach is useful when you are interested in testing association between two categorical variables – say exposure and disease – by adjusting for a third categorical variable. If done correctly, it also enables you to investigate whether the third variable is a confounder or an effect modifier.

How to control the effect of confounding variables?

There are various ways to modify a study design to actively exclude or control confounding variables (3) including Randomization, Restriction and Matching. In randomization the random assignment of study subjects to exposure categories to breaking any links between exposure and confounders.

What does Association mean in relation to confounding variables?

Basically, association means that the confounding variable is more common in the exposed group than the unexposed group (or vice versa), thus producing a statistical association. The confounder does not need to cause or prevent the exposure, it just needs to be disproportionately distributed between the exposed and unexposed groups.

What are the three criteria for confounding data?

Criteria for Confounders There are 3 criteria that a variable must meet in order for it to be a potential confounder (I say “potential” because not all variables that meet these criteria will actually turn out to confound the data—you figure this out during the analysis): The variable must be statistically associated with the exposure.