Bayesian approaches for design of psychometric studies with underreporting and misclassification.

Simple item page
dc.contributor.advisorStamey, James D.
dc.contributor.authorFalley, Brandi.
dc.contributor.departmentStatistical Sciences.en_US
dc.contributor.schoolsBaylor University. Dept. of Statistical Sciences.en_US
dc.date.accessioned2013-05-15T18:29:00Z
dc.date.available2013-05-15T18:29:00Z
dc.date.copyright2012-12
dc.date.issued2013-05-15
dc.description.abstractMeasurement error problems in binary regression are of considerable interest among researchers, especially in epidemiological studies. Misclassification can be considered a special case of measurement error specifically for the situation when measurement is the categorical classification of items. Bayesian methods offer practical advantages for the analysis of epidemiological data including the possibility of incorporating relevant prior scientific information and the ability to make inferences that do not rely on large sample assumptions. Because of the high cost and time constraints for clinical trials, researchers often need to determine the smallest sample size that provides accurate inferences for a parameter of interest. Although most experimenters have employed frequentist methods, the Bayesian paradigm offers a wide variety of methodologies and are becoming increasingly more popular in clinical trials because of their flexibility and their ease of interpretation. We will simultaneously estimate efficacy and safety where the safety variable is subject to underreporting. We propose a Bayesian sample size determination method to account for the underreporting and appropriately power the study. We will allow efficacy and safety to be independent, as well as dependent using a regression model. For both models, we will allow the safety variable to be underreported.en_US
dc.description.degreePh.D.en_US
dc.identifier.urihttp://hdl.handle.net/2104/8571
dc.language.isoen_USen_US
dc.publisheren
dc.rightsBaylor University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. Contact librarywebmaster@baylor.edu for inquiries about permission.en_US
dc.rights.accessrightsWorldwide access.en_US
dc.rights.accessrightsAccess changed 5/21/14.
dc.subjectMisclassification.en_US
dc.subjectUnderreporting.en_US
dc.subjectBayesian estimation.en_US
dc.subjectPsychometric studies.en_US
dc.titleBayesian approaches for design of psychometric studies with underreporting and misclassification.en_US
dc.typeThesisen_US

Files

Original bundle

Now showing 1 - 2 of 2
Thumbnail Image
Name:
Brandi_Falley_PhD.pdf
Size:
2.72 MB
Format:
Adobe Portable Document Format
Description:
Final dissertation
Download
No Thumbnail Available
Name:
Brandi_Falley_copyrightandavailabilityform.pdf
Size:
67.71 KB
Format:
Adobe Portable Document Format
Description:
Permission form
Download

License bundle

Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.87 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Electronic Theses and Dissertations
Theses/Dissertations - Statistical Sciences