Although researchers do their best to reduce error within every study, there will always be error. It is important to identify and report any possible error within the research study

Although researchers do their best to reduce error within every study, there will always be error. It is important to identify and report any possible error within the research study in order to accurately interpret the research study’s findings. In epidemiologic research, the focus is on assessing confounding and effect modification along with normal statistical measures (p-values, confidence intervals, etc.). For this Application Assignment, you will calculate and interpret the effects of confounding, random error, and effect modification within epidemiologic research. Read each of the following questions and answer them appropriately: a. A study of the relationship between exercise and heart attacks that is conducted among women who do not smoke. Explain whether gender is a confounder. b. A case-control study of the relationship between liver cirrhosis and alcohol use. In this study, smoking is associated with drinking alcohol and is a risk factor for liver cirrhosis among both non-alcoholics and alcoholics. Explain whether smoking is a confounder. a. An odds ratio of 1.2 (95% confidence interval: 0.8-1.5) is found for the association of low socioeconomic status and occurrence of obesity. b. A relative risk of 3.0 is reported for the association between consumption of red meat and the occurrence of colon cancer. The p-value of the association is 0.15. c. An odds ratio of 7 (95% confidence interval: 3.0 – 11.4) is found for the association of smoking and lung cancer. o Heavy Smoking—Cases: 450; Controls: 200 Calculate the appropriate measure of association between alcohol use and CHD in both smokers and non-smokers. Discuss whether smoking was a confounder of the association. is the relationship of alcohol use to CHD after controlling for confounding? Calculate and interpret the appropriate measure of association between driver’s education and accidents. The question arose as to whether gender might be an effect modifier of this association. When gender was assessed, the data looked like the following: Perform the appropriate calculations to test for effect modification. Interpret your results.

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