Jolynn - Week 3 Individual Paper

Jolynn - Week 3 Individual Paper - Decision of Uncertainty...

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Decision of Uncertainty 1 Decision of Uncertainty Paper Decision of Uncertainty Jolynn Weeks University of Phoenix Patricia Towne QNT 561 April 13, 2011
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Decision of Uncertainty 2 Decision of Uncertainty “Since ovarian cancer is the fifth leading cause of all cancer deaths in women, the goal of effective ovarian cancer screening would be to detect early stage disease with a subsequent improvement in overall survival” (Runowicz, 1999). Ovarian cancer is a disease that touches many lives. In my eyes, the most important part of medicine is diagnostic testing which can catch and prevent such diseases. One of the most important diagnostic tests for ovarian cancer known is the CA-125 screening. The CA-125, known as a tumor-marker, is a protein found in cells, which is in greater concentration when a tumor is present (Stoppler). When analyzing the effectiveness of CA-125 diagnostic test, we would consider the sensitivity and specificity. In this case, the sensitivity of the test is the proportion of results that correctly identify people with ovarian cancer. In other words, people testing positive represent, in symbolic terms, P. The specificity is the percentage of tests that correctly classify people who do not have ovarian cancer. This paper will analyze the probability of people testing positive for ovarian cancer. From a detection and treatment perspective this is extremely important, as is for the medical business as well. Assume a person goes to the doctor for a CA-125 ovarian cancer screening, and they test positive. The question would be what is the probability that the person being tested has ovarian cancer? By using Bayes’ Theorem we can determine the result. In order to complete this calculation, the sensitivity, specificity, and positive predictive values (ppv) need to be determined. Sensitivity refers to the probability that a specific test will actually be positive in a person who has the disease. Specificity, on the other hand, refers to the probability that the specific test being administered will be negative is a person without the disease. Lastly, the positive
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Decision of Uncertainty 3 predictive value is the probability that the person that comes back with a positive result on the test actually has the disease. For example, a test that identified everyone who took it has having ovarian cancer would have perfect sensitivity, but it would have a very high false alarm rate, or a diagnostic screening that identifies everyone has being cancer-free would have perfect specificity, but it would fail to diagnose everyone who has the disease. We can use Bayes’ Theorem to determine the probability of this research. To use this
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This note was uploaded on 07/18/2011 for the course QNT 561 taught by Professor Rivera during the Spring '11 term at University of Phoenix.

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Jolynn - Week 3 Individual Paper - Decision of Uncertainty...

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