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Detecting Outliers and Influential Data Points in Receiver Operating Characteristic (ROC) Analysis

Klym, Amy H (2007) Detecting Outliers and Influential Data Points in Receiver Operating Characteristic (ROC) Analysis. Master's Thesis, University of Pittsburgh. (Unpublished)

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Abstract

<p style="margin: 0in 0in 0pt; text-indent: 0in" class="MsoNormal"><font face="Times New Roman" size="3">Receiver operating characteristic (ROC) studies and analyses are often used to evaluate medical tests and are very useful in the field of radiology to evaluate a single diagnostic imaging system, to compare the accuracy of two or more diagnostic imaging systems, or to assess observer performance.<span>  </span>There have been many refinements in the development of different ROC type study designs and the corresponding statistical analysis. These methods have become increasingly important and ROC methods are the principal approach for evaluating imaging technologies and/or observer performances. The systems that are often evaluated using ROC methodology include digital and radiographic images of the chest and breast. An improved method of evaluating diagnostic imaging systems contributes to the development of better diagnostic methods; hence, improving imaging systems for diagnoses of breast and lung cancer would have major public health significance. In our work with observer performance studies, in which receiver operating characteristic (ROC) analysis is used, we have noted that some contributions of readers and cases can substantially alter the conclusions of the analysis.<span>  </span>To the best of our knowledge, to date there is no statistical test cited in the statistical literature that addresses the detection and influence of outliers on the estimate of the area under the ROC curve.<span>  </span>Evaluating outliers may be especially important for the ROC model since subtle (difficult) cases have the potential for being missed by a reader (e.g. a difficult positive case is rated as an unquestionably negative case), and can have a considerable influence on the estimated area under the ROC curve, especially if the study has a small set of cases.<span>  </span>Therefore, we believe it is important to develop a method for detecting and measuring the influence of outliers for ROC models.<span>  </span>The development of this method will involve deriving a test statistic for outliers based on the jackknife influence values and conducting a preliminary validation of the test.</font></p>


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Details

Item Type: University of Pittsburgh ETD
Status: Unpublished
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Klym, Amy Hklymah@upmc.edu
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairRockette, Howard E.herbst@pitt.eduHERBST
Committee MemberBandos, Andriy I.anb61@pitt.eduANB61
Committee MemberGur, Davidgurd@upmc.edu
Date: 28 June 2007
Date Type: Completion
Defense Date: 28 February 2007
Approval Date: 28 June 2007
Submission Date: 23 March 2007
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Institution: University of Pittsburgh
Schools and Programs: Graduate School of Public Health > Biostatistics
Degree: MS - Master of Science
Thesis Type: Master's Thesis
Refereed: Yes
Uncontrolled Keywords: power; pseudovalues; simulations; type I error
Other ID: http://etd.library.pitt.edu/ETD/available/etd-03232007-105346/, etd-03232007-105346
Date Deposited: 10 Nov 2011 19:32
Last Modified: 15 Nov 2016 13:37
URI: http://d-scholarship.pitt.edu/id/eprint/6561

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