In the English language, “to confound” means “to confuse or distort.” In the language of statistics, “confounding” refers to possession of a distorted view of the relationship between variables. A confounder, thus, is a variable that distorts the relationship of two other variables.
Consider the following two examples of variables that have distorted other relationships:
- The use of cigarettes distorted the relationship of coffee drinking to bladder cancer. You could easily overestimate the relationship of coffee and bladder cancer if you did not also consider cigarette use. Cigarette use is related to both coffee drinking and bladder cancer and is, therefore, a confounder of that relationship.
- The seasonal effect of summer distorted the relationship of ice cream and polio. Researchers believed that the exposure to ice cream increased the risk of polio among children. In reality, polio was more active in summer and children ate more ice cream in summer so that relationship was confounded by summer.
In the examples from the weekly introduction, the original research did not consider the confounder and, therefore, reached spurious conclusions. In your future research, you need to be able to identify both measured and non-measured variables that could confound the relationships you wish to evaluate.
For this Discussion, select a peer-reviewed article from the Walden library that identifies an exposure and disease that appear to be related. Review the measured confounders and the effects that they have on that relationship.
With these thoughts in mind:
Post by Day 4 a brief description of the article that you selected, including an explanation of the relationship between exposure and disease. Describe the confounders that the authors identified. Describe a variable not measured in the article that might also confound the relationship between exposure and disease and explain why. Explain one potential effect that the non-measured variable might have on the relationship and explain how. Explain one way that you could counteract the effects of that non-measured variable.
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