Statistical methods for diagnostic testing: an illustration using a new method for cancer detection

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dc.contributor.author Sun, Xin
dc.date.accessioned 2013-10-16T18:20:08Z
dc.date.available 2013-10-16T18:20:08Z
dc.date.issued 2013-10-16
dc.identifier.uri http://hdl.handle.net/2097/16679
dc.description.abstract This report illustrates how to use two statistic methods to investigate the performance of a new technique to detect breast cancer and lung cancer at early stages. The two methods include logistic regression and classification and regression tree (CART). It is found that the technique is effective in detecting breast cancer and lung cancer, with both sensitivity and specificity close to 0.9. But the ability of this technique to predict the actual stages of cancer is low. The age variable improves the ability of logistic regression in predicting the existence of breast cancer for the samples used in this report. But since the sample sizes are small, it is impossible to conclude that including the age variable helps the prediction of breast cancer. Including the age variable does not improve the ability to predict the existence of lung cancer. If the age variable is excluded, CART and logistic regression give a very close result. en_US
dc.language.iso en_US en_US
dc.publisher Kansas State University en
dc.subject Logistic regression en_US
dc.subject Cancer detection en_US
dc.title Statistical methods for diagnostic testing: an illustration using a new method for cancer detection en_US
dc.type Report en_US
dc.description.degree Master of Science en_US
dc.description.level Masters en_US
dc.description.department Department of Statistics en_US
dc.description.advisor Gary Gadbury en_US
dc.subject.umi Statistics (0463) en_US
dc.date.published 2013 en_US
dc.date.graduationmonth December en_US


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