March 11 - POPM*3240 March 11th Odds ratios Odds of...

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POPM*3240 March 11 th Odds ratios - Odds of exposure in disease positive vs. disease negative - Odds of disease in exposure positive vs. exposure negative - How you interpret depends on study - Case control talking about the odds of exposure because we selected on the basis of disease o Odds of not being vaccinated among cases of influenza were 2 times higher amongst those vaccinated - Cohort/cross-sectional odds of disease Multivariable Models Confounding – type of bias - Effects of a third factor get muddled up in association - If we don’t control we get a biased measure of association o Away from null, or towards null - Example: exercise and its association with heart disease o Age is associated with exercise o Age is higher in people with heart disease o Age is a potential confounding variable Related to exposure and outcome But not a consequence of exposure (exercise) o Doing study – want to control for the effects of age Multivariable modeling! Methods to Control Confounding - Randomization is equalizing confounders (hopefully) o Should make sure randomization process works - Restriction/exclusion o Only look at one level of the confounding variable o Reduce who we can extrapolate the results to - Matching o
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  • Fall '13
  • Regression Analysis, heart girth, Multivariable modeling, donkey weight, potential confounding effect

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