What is Stratified Analysis?
Stratified analysis is a statistical method used in
epidemiology to control for confounding by dividing a dataset into subgroups, or
strata, based on certain characteristics such as age, gender, or exposure level. This allows researchers to examine relationships within each stratum, thereby providing a clearer understanding of the association between exposure and outcome.
Why is Stratified Analysis Important?
Stratified analysis is crucial because it helps to
control for confounding factors that could otherwise obscure the true relationship between the exposure and the outcome. By examining subgroups separately, researchers can identify whether the observed association is consistent across different strata or if it varies, indicating potential effect modification.
When Should Stratified Analysis be Used?
Stratified analysis should be used when there is a possibility of confounding or effect modification. Confounding occurs when an extraneous variable is associated with both the exposure and the outcome, potentially distorting the observed relationship. Effect modification, on the other hand, occurs when the effect of the primary exposure on the outcome differs depending on the level of another variable.
Identify potential confounders or effect modifiers.
Divide the data into strata based on these variables.
Calculate the measure of association (e.g., odds ratio, risk ratio) within each stratum.
Compare the measures of association across strata to determine if they are consistent or if there is evidence of effect modification.
If consistent, combine the stratum-specific estimates using techniques such as the
Mantel-Haenszel method.
Examples of Stratified Analysis
Consider a study investigating the relationship between smoking and lung cancer. Age is a potential confounder because it is associated with both smoking and lung cancer risk. By stratifying the data into different age groups, researchers can examine the association between smoking and lung cancer within each age group, reducing the impact of age as a confounding factor.
Another example is a study on the effect of physical activity on heart disease, stratified by gender. If the effect of physical activity on heart disease differs between men and women, this indicates effect modification, and the association should be reported separately for each gender.Limitations of Stratified Analysis
While stratified analysis is a powerful tool, it has limitations: Limited sample size: Dividing data into multiple strata can result in small sample sizes within each stratum, reducing statistical power.
Multiple stratification variables: Including too many stratification variables can lead to a large number of strata, complicating analysis and interpretation.
Residual confounding: Stratified analysis may not completely eliminate confounding if the strata are not perfectly homogeneous with respect to the confounder.
Conclusion
Stratified analysis is an essential method in epidemiology for controlling confounding and identifying effect modification. By dividing data into subgroups based on relevant characteristics, researchers can obtain a clearer understanding of the relationship between exposure and outcome. However, it is important to be aware of its limitations, including reduced sample sizes and potential for residual confounding.
