Advanced Healthcare Statistics Practice Test

In the context of examining the relationship between annual income and cholesterol level while controlling for age as a potential confounder, the appropriate method to use is partial correlation. Partial correlation allows researchers to analyze the direct relationship between two variables—in this case, annual income and cholesterol level—while holding one or more other variables—in this case, age—constant. This technique effectively removes the influence of age, enabling a clearer assessment of the correlation between the primary variables of interest.

The importance of controlling for confounding variables like age lies in the fact that they can distort the perceived relationship between the primary variables. By utilizing partial correlation, researchers can ensure that any observed correlation is not confounded by age, leading to more accurate and reliable interpretations of the data.

Kendall's Tau, Spearman correlation, and Point-Biserial are methods that assess different types of relationships or include data of different types but do not account for the influence of confounders in the same way that partial correlation does. Therefore, if the aim is specifically to remove the influence of a confounder while assessing the relationship between two variables, partial correlation is the most suitable choice.