{"id":{"repo_id":"buffalo","oai_identifier":"oai:ubir.buffalo.edu:10477/84086"},"canonical_url":"https://search.dev.ndltd.org/etd/buffalo/oai:ubir.buffalo.edu:10477/84086","repository":{"repo_id":"buffalo","name":"Buffalo","base_url":"https://ubir.buffalo.edu/oai/request"},"display":{"title":"Confidence Interval Methods of Association Parameters for Correlated Bilateral Data","abstract":"Ph.D.","abstract_html":"Ph.D.","abstract_has_math":false,"creators":["Peng, Xuan"],"institution":"State University of New York at Buffalo","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Ma, Chang-Xing","Biostatistics"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-06-21T15:47:47Z","date_published":"2022-06-21T15:47:47Z","updated_at":"2026-07-27T19:05:30Z","subjects":["biostatistics"],"languages":["eng"],"rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/10477/84086","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Ma, Chang-Xing","Biostatistics"]},{"key":"dc:creator","label":"Author","values":["Peng, Xuan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-06-21T15:47:47Z","2020"]},{"key":"dc:publisher","label":"Institution","values":["State University of New York at Buffalo"]},{"key":"dc:type","label":"Dc Type","values":["Text","Dissertation"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["biostatistics"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/10477/84086"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Ph.D.","In many medical researches (e.g., ophthalmological and otolaryngology studies), clinical data often include bilateral measurements from paired organs (e.g., eyes and ears). Previous studies showed that ignoring the intraclass correlation for such bilateral data could lead to biased inference. Besides hypothesis testing, confidence interval (CI) method is also an active research field and provide more information thus it is our study focus. In this dissertation, we first propose CI methods for proportion ratio between two groups for correlated bilateral data. And then we extend the methods to multiple groups and propose simultaneous CI methods for odds ratio between one control group and multiple treatment groups. In addition, we also propose CI methods for odds ratio between two groups with consideration of stratification effect. The performance of the resulting CIs and SCIs are investigated in Monte Carlo simulation studies with respect to their empirical coverage probability and mean interval width. Real work examples are included to illustrate the application of the proposed methodologies."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Confidence Interval Methods of Association Parameters for Correlated Bilateral Data"]}]}],"canonical_facts":{"dc:contributor":["Ma, Chang-Xing","Biostatistics"],"dc:creator":["Peng, Xuan"],"dc:date":["2022-06-21T15:47:47Z","2020"],"dc:description":["Ph.D.","In many medical researches (e.g., ophthalmological and otolaryngology studies), clinical data often include bilateral measurements from paired organs (e.g., eyes and ears). Previous studies showed that ignoring the intraclass correlation for such bilateral data could lead to biased inference. Besides hypothesis testing, confidence interval (CI) method is also an active research field and provide more information thus it is our study focus. In this dissertation, we first propose CI methods for proportion ratio between two groups for correlated bilateral data. And then we extend the methods to multiple groups and propose simultaneous CI methods for odds ratio between one control group and multiple treatment groups. In addition, we also propose CI methods for odds ratio between two groups with consideration of stratification effect. The performance of the resulting CIs and SCIs are investigated in Monte Carlo simulation studies with respect to their empirical coverage probability and mean interval width. Real work examples are included to illustrate the application of the proposed methodologies."],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/10477/84086"],"dc:language":["eng"],"dc:publisher":["State University of New York at Buffalo"],"dc:rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."],"dc:subject":["biostatistics"],"dc:title":["Confidence Interval Methods of Association Parameters for Correlated Bilateral Data"],"dc:type":["Text","Dissertation"]},"updated_at":"2026-07-27T19:05:30Z"}