The link between the Reserpine, breast cancer and obesity and the role each play and how they affect each other is a great example.

Confounding is often discussed to as mixing of effects wherein the effects of the exposure under study on a given outcome are mixed in with the effects of an added issue or set of causes resulting in a distortion of the true relationship. This can happen in a clinical trial when the delivery of a known analytical factor differs between groups being linked. The actuality of confounding variables in studies makes it challenging to establish a clear causal link between treatment and outcome unless suitable methods are used to adjust for the effect of the confounders. Confounding variables are those that may compete with the exposure of interest such as treatment in explaining the outcome of a study (Skelly, Dettori & Brod 2012).The first insight learned about confounding, based on the “Multicausality: Confounding assignment is the investigation of the causes of diseases and the established links between risks factors and health. The link between the Reserpine, breast cancer and obesity and the role each play and how they affect each other is a great example.Secondly, retrospective cohort study was also used to find out the relative risks for chronic obstructive pulmonary disease (COPD) due to smoking. After 20 years of study, it was concluded that COPD was due to smoking after linking copper smelters and truck maintenance workers. The cohort study that is used to observe large groups of people and also records the exposure to certain risks factors to find clues as to what is the possible cause of the disease.ReferenceSkelly, A. C., Dettori, R. J. & Brod, E. D. (2012). Assessing bias: the importance of considering confounding. Evidence-based spine care Journal; 3(1): 9–12.