Konfoundointitekijä

Konfoundointitekijä, often translated as confounding factor or confounder, is a concept used in research, particularly in statistics and epidemiology, to describe a variable that influences both the dependent variable and the independent variable. This extraneous variable can distort the relationship between the two variables being studied, leading to an erroneous conclusion about their association. Essentially, a confounder creates a misleading association where one may not truly exist, or it can mask a true association.

To be considered a confounder, a variable must meet three criteria: it must be associated with the exposure (independent variable), it must be associated with the outcome (dependent variable), and it must not be on the causal pathway between the exposure and the outcome. For example, if studying the link between coffee consumption and heart disease, smoking could be a confounder. Smokers are more likely to drink coffee and smoking also independently increases the risk of heart disease. Without accounting for smoking, one might wrongly attribute the increased heart disease risk solely to coffee. Researchers use various statistical methods, such as stratification, matching, or regression analysis, to control for confounders and obtain a more accurate understanding of the true relationship between the variables of interest.