A Brief History of Disease, Science and Medicine by Michael Kennedy

By Michael Kennedy

This advent to the background of drugs starts off with the evolution of infectious ailments on the finish of the final ice age. It describes the beginning of technology and drugs in historical civilizations, together with China and India. the 1st 3rd of the publication covers the early interval that's thought of the "classical" background of medication. the remaining describes the evolution of contemporary drugs and surgical procedure as much as the current. the ultimate bankruptcy is a heritage of clinical economics and explains the beginning of medical health insurance, HMOs and scientific malpractice proceedings, topics defined nowhere else within the scientific tuition curriculum.

There is a forty web page index and over 550 footnotes, so much of them references to the unique articles defined within the textual content. A bibliography of crucial assets can also be integrated.

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Example text

Given a model for simple analysis: a. state an expression for the odds ratio describing the exposure–disease relationship b. state or recognize the null hypothesis of no exposure–disease relationship in terms of parameter(s) of the model c. compute or recognize an expression for the risk for exposed or unexposed persons separately d. compute or recognize an expression for the odds of getting the disease for exposed or unexposed persons separately 3. Given two (0, 1) independent variables: a. state or recognize a logistic model that allows for the assessment of interaction on a multiplicative scale b.

The E, V, W model – A general model containing a (0, 1) exposure and potential confounders and effect modifiers (pages 55–64) Objectives Objectives 43 Upon completion of this chapter, the learner should be able to: 1. State or recognize the logistic model for a simple analysis. 2. Given a model for simple analysis: a. state an expression for the odds ratio describing the exposure–disease relationship b. state or recognize the null hypothesis of no exposure–disease relationship in terms of parameter(s) of the model c.

Given a fitted logistic model from a casecontrol study, an odds ratio can be estimated. Given a fitted logistic model from a casecontrol study, we can estimate a risk ratio if the rare disease assumption is appropriate. The logit transformation for the logistic model gives the log odds ratio for the comparison of two groups. The constant term, a, in the logistic model can be interpreted as a baseline log odds for getting the disease. The coefficient bi in the logistic model can be interpreted as the change in log odds corresponding to a one unit change in the variable Xi that ignores the contribution of other variables.

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