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

dc.contributor.authorSun, Xin
dc.date.accessioned2013-10-16T18:20:08Z
dc.date.available2013-10-16T18:20:08Z
dc.date.graduationmonthDecemberen_US
dc.date.issued2013-10-16
dc.date.published2013en_US
dc.description.abstractThis 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.description.advisorGary L. Gadburyen_US
dc.description.degreeMaster of Scienceen_US
dc.description.departmentDepartment of Statisticsen_US
dc.description.levelMastersen_US
dc.identifier.urihttp://hdl.handle.net/2097/16679
dc.language.isoen_USen_US
dc.publisherKansas State Universityen
dc.subjectLogistic regressionen_US
dc.subjectCancer detectionen_US
dc.subject.umiStatistics (0463)en_US
dc.titleStatistical methods for diagnostic testing: an illustration using a new method for cancer detectionen_US
dc.typeReporten_US

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