Although the two terms are discussed separately in many textbooks, some other epidemiologists suggest that confounding is a type of bias.
It is worth considering their perspective and exploring the relationship between these concepts. By understanding how confounding and bias are related, we can improve our research methods and draw more accurate conclusions.
Dear Sir Mohamed Hussein Adam This is indeed a fascinating question that has sparked debate among epidemiologists and researchers. Thank you for asking this question. Let's delve into it further.
Confounding and bias are related concepts in epidemiology, but they are not synonymous.
Confounding occurs when a third variable, called a confounder, is associated with both the exposure and the outcome of interest, biasing the observed association between the exposure and the outcome.
Bias, on the other hand, refers to systematic errors or deviations from the truth in the design, conduct, or analysis of a study that can lead to erroneous conclusions.
While confounding can potentially bias the results of a study, not all confounding leads to bias. If confounding is adequately controlled for through study design or statistical methods, it may not introduce bias. For example, if researchers adjust for the confounding variable in their analysis or use techniques like stratification or multivariable regression, they can mitigate the impact of confounding and obtain unbiased estimates of the exposure-outcome association.
Let's look at an example: Imagine a study shows that smoking and drinking coffee are linked to high blood pressure. But here's the catch: Both smoking and coffee drinking are also connected to how much money people make, which is a known risk factor for high blood pressure. So, money (or socioeconomic status) is like the middleman here, messing with the results. It's related to both smoking and coffee drinking, as well as high blood pressure. That's what we call a confounder.
If researchers fail to account for SES in their analysis, the observed association between smoking/coffee consumption and arterial hypertension may be confounded and biased. However, if they adjust for SES in their statistical analysis, they can control for confounding and obtain unbiased estimates of the true associations between smoking, coffee consumption, and arterial hypertension.
By recognizing the distinction between confounding and bias and implementing appropriate methods to address confounding in study design and analysis, researchers can enhance the validity and reliability of their findings. Here is my humble answers from my point of view, i'm open for further discussion. Hope it helps. I wish you success in all your endeavors.
Reference:
Rothman, K., Greenland, S., & Lash, TL. (2008). Modern Epidemiology, 3rd Edition. Lippincott Williams & Wilkins.
Thank you Ronald Pratama Adiwinoto for the valuable information. It's greatly beneficial.
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