Indirect standardization is a statistical method used to compare rates or proportions between two or more groups while controlling for the effects of confounding variables. This method is commonly used in epidemiological studies to compare the incidence or prevalence of a certain disease or condition between different population groups.

An example of indirect standardization is a study that compares the rates of breast cancer between two different ethnic groups, such as African American and White women. In this study, the researchers would first calculate the overall incidence rate of breast cancer in the general population. This would be the standard population rate. Next, the researchers would calculate the incidence rate of breast cancer in each ethnic group. This would be the observed rate. The researchers would then use the standard population rate to adjust for any differences in the distribution of age between the two ethnic groups. This would provide a more accurate comparison of the rates of breast cancer between the two ethnic groups, controlling for the effect of age on the incidence of breast cancer.

Another example of indirect standardization is a study that compares the mortality rates of lung cancer between two different cities, such as Los Angeles and New York. In this study, the researchers would first calculate the overall mortality rate of lung cancer in the general population. This would be the standard population rate. Next, the researchers would calculate the mortality rate of lung cancer in each city. This would be the observed rate. The researchers would then use the standard population rate to adjust for any differences in the distribution of smoking prevalence between the two cities. This would provide a more accurate comparison of the mortality rates of lung cancer between the two cities, controlling for the effect of smoking on the mortality rate of lung cancer.

Overall, indirect standardization is a useful statistical method for comparing rates or proportions between different groups while controlling for the effects of confounding variables. This method allows researchers to isolate the effect of the variable of interest on the outcome being studied, providing a more accurate and reliable comparison between groups.