{"id":{"repo_id":"buffalo","oai_identifier":"oai:ubir.buffalo.edu:10477/79349"},"canonical_url":"https://search.dev.ndltd.org/etd/buffalo/oai:ubir.buffalo.edu:10477/79349","repository":{"repo_id":"buffalo","name":"Buffalo","base_url":"https://ubir.buffalo.edu/oai/request"},"display":{"title":"Statistical Inference of Association Parameters for Stratified Bilateral Correlated Data","abstract":"Ph.D.","abstract_html":"Ph.D.","abstract_has_math":false,"creators":["Shen, Xi; 0000-0002-3136-8487"],"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":2019,"date_issued":"2019-04-04T20:30:41Z","date_published":"2019-04-04T20:30:41Z","updated_at":"2026-07-27T19:05:16Z","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/10477/79349","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":["Shen, Xi; 0000-0002-3136-8487"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2019-04-04T20:30:41Z","2019","2018-12-18 15:18:56"]},{"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/10477/79349"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Ph.D.","Bilateral correlated data are often encountered in medical researches such as ophthalmologic (or otolaryngologic) studies, in which each unit contributes information from paired organs to the data analysis, and the measurements from such paired organs are generally highly correlated. Various statistical methods have been developed to tackle intra-class correlation on bilateral correlated data analysis. In practice, the center-effect or confounding-effect could lead to imbalance with treatment arms, making it necessary to adjust for stratification/confounding factors in the data analysis. Therefore, either ignoring the intra-class correlation or confounding effect may lead to biased results.In this dissertation, we first derive several approaches for testing homogeneity of risk difference for stratified bilateral correlated data under the assumption of equal correlation. Further, we propose several procedures for testing equality of difference of two proportions in a stratified bilateral design. Then, we extend the methodologies to construct a variety of confidence intervals(CIs) for estimating difference in the proportions of responders. In addition, we propose three approaches for testing homogeneity of odds ratio for stratified bilateral correlated data under the assumption of equal correlation. The performance of the proposed test methods and CI estimations is evaluated by Monte Carlo simulations. We use real data to illustrate the practical implementation of the proposed methodologies as well."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Statistical Inference of Association Parameters for Stratified Bilateral Correlated Data"]}]}],"canonical_facts":{"dc:contributor":["Ma, Chang-Xing","Biostatistics"],"dc:creator":["Shen, Xi; 0000-0002-3136-8487"],"dc:date":["2019-04-04T20:30:41Z","2019","2018-12-18 15:18:56"],"dc:description":["Ph.D.","Bilateral correlated data are often encountered in medical researches such as ophthalmologic (or otolaryngologic) studies, in which each unit contributes information from paired organs to the data analysis, and the measurements from such paired organs are generally highly correlated. Various statistical methods have been developed to tackle intra-class correlation on bilateral correlated data analysis. In practice, the center-effect or confounding-effect could lead to imbalance with treatment arms, making it necessary to adjust for stratification/confounding factors in the data analysis. Therefore, either ignoring the intra-class correlation or confounding effect may lead to biased results.In this dissertation, we first derive several approaches for testing homogeneity of risk difference for stratified bilateral correlated data under the assumption of equal correlation. Further, we propose several procedures for testing equality of difference of two proportions in a stratified bilateral design. Then, we extend the methodologies to construct a variety of confidence intervals(CIs) for estimating difference in the proportions of responders. In addition, we propose three approaches for testing homogeneity of odds ratio for stratified bilateral correlated data under the assumption of equal correlation. The performance of the proposed test methods and CI estimations is evaluated by Monte Carlo simulations. We use real data to illustrate the practical implementation of the proposed methodologies as well."],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/10477/79349"],"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":["Statistical Inference of Association Parameters for Stratified Bilateral Correlated Data"],"dc:type":["Text","Dissertation"]},"updated_at":"2026-07-27T19:05:16Z"}