{"id":{"repo_id":"buffalo","oai_identifier":"oai:ubir.buffalo.edu:10477/86758"},"canonical_url":"https://search.dev.ndltd.org/etd/buffalo/oai:ubir.buffalo.edu:10477/86758","repository":{"repo_id":"buffalo","name":"Buffalo","base_url":"https://ubir.buffalo.edu/oai/request"},"display":{"title":"Selected Topics in Diagnostic Studies, Biomarker Evaluation and Beyond","abstract":"Ph.D.","abstract_html":"Ph.D.","abstract_has_math":false,"creators":["Hua, Jia"],"institution":"State University of New York at Buffalo","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Tian, Lili","Biostatistics"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-02-21T21:44:48Z","date_published":"2025-02-21T21:44:48Z","updated_at":"2026-07-27T19:05:37Z","subjects":["public health","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/10477/86758","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Tian, Lili","Biostatistics"]},{"key":"dc:creator","label":"Author","values":["Hua, Jia"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-02-21T21:44:48Z","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":["public health","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/10477/86758"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Ph.D.","In the simplest setting of diagnostic tests, the outcomes are dichotomous, either healthy or diseased. For many diseases such as Alzheimer's disease, there usually exist one or more intermediate stages between healthy and fully diseased stages. Therefore, accurate multi-category classification and biomarker evaluation under such settings are of paramount importance for biomedical and clinical research. This thesis mainly aims to develop novel and optimal classification methods as well as new biomarker combination methods in multi-class setting. These methods are expected to have broad applicability in practice. Several machine learning (ML) methods have gained their popularity in a variety of fields; however, little connection has been built and inspected between machine learning methods and statistical methods for biomarker evaluation. This thesis work also aims to fill such gap. Specifically, this thesis work consists of three parts: 1) to present several unexploited methods for cut-point selection which are strong competitors against existing ones; 2) to propose several biomarker combination methods which are more efficient and superior to the existing ones; and finally 3) to compare some popular machine learning methods with proposed biomarker combination methods in binary classification setting.","**To request an accessible version of the file(s) associated with this item, contact library@buffalo.edu. Please include the item's persistent URL [http://hdl.handle.net/. . .] in your request.**"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Selected Topics in Diagnostic Studies, Biomarker Evaluation and Beyond"]}]}],"canonical_facts":{"dc:contributor":["Tian, Lili","Biostatistics"],"dc:creator":["Hua, Jia"],"dc:date":["2025-02-21T21:44:48Z","2020"],"dc:description":["Ph.D.","In the simplest setting of diagnostic tests, the outcomes are dichotomous, either healthy or diseased. For many diseases such as Alzheimer's disease, there usually exist one or more intermediate stages between healthy and fully diseased stages. Therefore, accurate multi-category classification and biomarker evaluation under such settings are of paramount importance for biomedical and clinical research. This thesis mainly aims to develop novel and optimal classification methods as well as new biomarker combination methods in multi-class setting. These methods are expected to have broad applicability in practice. Several machine learning (ML) methods have gained their popularity in a variety of fields; however, little connection has been built and inspected between machine learning methods and statistical methods for biomarker evaluation. This thesis work also aims to fill such gap. Specifically, this thesis work consists of three parts: 1) to present several unexploited methods for cut-point selection which are strong competitors against existing ones; 2) to propose several biomarker combination methods which are more efficient and superior to the existing ones; and finally 3) to compare some popular machine learning methods with proposed biomarker combination methods in binary classification setting.","**To request an accessible version of the file(s) associated with this item, contact library@buffalo.edu. Please include the item's persistent URL [http://hdl.handle.net/. . .] in your request.**"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/10477/86758"],"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":["public health","biostatistics"],"dc:title":["Selected Topics in Diagnostic Studies, Biomarker Evaluation and Beyond"],"dc:type":["Text","Dissertation"]},"updated_at":"2026-07-27T19:05:37Z"}