False positives, Sensitivity and Specificity. Bayes formula is used routinely in

Question

False positives, Sensitivity and Specificity. Bayes formula is used routinely in epidemiology. Suppose we deal with a disease and we have test for the disease. We know The sensitivity of the test is 99%, this means that if you have the disease, the test is positive with probability 0.99. The specificity of the test is 98%, this means that if you do not have the disease, the test is negative with probability 0.98. The prevalence of the disease is one in two hundred, this means that the probability to carry the disease is 0.005. The probability that someone with a positive test actually has the disease is called the positive predictive value of the test. The probability that someone with a negative test actually does not have the disease is called the negative predictive value of the test. Express the positive and negative predictive value of the test using conditional probabilities and compute them using Bayes formula.

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False positives, Sensitivity and Specificity. Bayes formula is used routinely in

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