{"id":{"repo_id":"uwo","oai_identifier":"oai:uwo.scholaris.ca:20.500.14721/31457"},"canonical_url":"https://search.dev.ndltd.org/etd/uwo/oai:uwo.scholaris.ca:20.500.14721/31457","repository":{"repo_id":"uwo","name":"Western University","base_url":"https://uwo.scholaris.ca/server/oai/request"},"display":{"title":"Sample Size Formulas For Estimating Areas Under the Receiver Operating Characteristic Curves With Precision and Assurance","abstract":"The area under the receiver operating characteristic curve (AUC) is commonly used to quantify the discriminative ability of tests with ordinal or continuous test data. When planning a study to evaluate a new test, it is important to determine a minimum sample size required to achieve a prespecified precision of estimating AUC. However, conventional sample size formulas do not consider the probability of achieving a prespecified precision, resulting in underestimation of sample sizes. To incorporate the assurance probability, asymptotic sample size formulas were derived using different variance estimators for AUC in this thesis. The precision of AUC estimations was quantified by either lower confidence limits or interval width. The performance of proposed sample size formulas was evaluated through simulation studies. Simulation results show that the formula based on lower limits with the nonparametric method performs best and can be used with both ordinal and continuous data. The methods are illustrated with examples from previously published data.","abstract_html":"The area under the receiver operating characteristic curve (AUC) is commonly used to quantify the discriminative ability of tests with ordinal or continuous test data. When planning a study to evaluate a new test, it is important to determine a minimum sample size required to achieve a prespecified precision of estimating AUC. However, conventional sample size formulas do not consider the probability of achieving a prespecified precision, resulting in underestimation of sample sizes. To incorporate the assurance probability, asymptotic sample size formulas were derived using different variance estimators for AUC in this thesis. The precision of AUC estimations was quantified by either lower confidence limits or interval width. The performance of proposed sample size formulas was evaluated through simulation studies. Simulation results show that the formula based on lower limits with the nonparametric method performs best and can be used with both ordinal and continuous data. The methods are illustrated with examples from previously published data.","abstract_has_math":false,"creators":["Lu, Grace"],"institution":"The University of Western Ontario","degree_name":"M Sc","degree_level":null,"degree_discipline":"Epidemiology and Biostatistics","degree_department":null,"school":null,"contributors":[],"advisors":["Choi, Yun-Hee","Zou, Guangyong"],"committee_chairs":[],"committee_members":[],"year":2021,"date_issued":"2021-06-01","date_published":"2021-06-01","updated_at":"2026-07-27T21:56:09Z","subjects":["assurance probability","confidence interval","variance"],"languages":["en_ca"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/20.500.14721/31457","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Choi, Yun-Hee","Zou, Guangyong"]},{"key":"dc:creator","label":"Author","values":["Lu, Grace"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-07-10T19:19:35Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-07-10T19:19:35Z"]},{"key":"dc:date.issued","label":"Date","values":["2021-06-01"]},{"key":"dc:publisher","label":"Institution","values":["The University of Western Ontario"]},{"key":"dc:type","label":"Dc Type","values":["thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Epidemiology and Biostatistics"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M Sc"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["assurance probability","confidence interval","variance"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en_ca"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/20.500.14721/31457"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Collaborative Specialization: Biostatistics","The thesis cover page in the PDF document includes references to Western University’s previous institutional repository platform, known as Scholarship@Western, and links to that platform (beginning with ir.lib.uwo.ca). In citing or referring to this thesis, use the DOI or handle from this page instead. Sample citation: Author name, \"Thesis title.\" (Year). Western University Open Repository. https://doi.org/10.71858/123456."]},{"key":"dc:description.abstract","label":"Abstract","values":["The area under the receiver operating characteristic curve (AUC) is commonly used to quantify the discriminative ability of tests with ordinal or continuous test data. When planning a study to evaluate a new test, it is important to determine a minimum sample size required to achieve a prespecified precision of estimating AUC. However, conventional sample size formulas do not consider the probability of achieving a prespecified precision, resulting in underestimation of sample sizes. To incorporate the assurance probability, asymptotic sample size formulas were derived using different variance estimators for AUC in this thesis. The precision of AUC estimations was quantified by either lower confidence limits or interval width. The performance of proposed sample size formulas was evaluated through simulation studies. Simulation results show that the formula based on lower limits with the nonparametric method performs best and can be used with both ordinal and continuous data. The methods are illustrated with examples from previously published data."]},{"key":"dc:title","label":"Title","values":["Sample Size Formulas For Estimating Areas Under the Receiver Operating Characteristic Curves With Precision and Assurance"]}]}],"canonical_facts":{"dc:contributor.advisor":["Choi, Yun-Hee","Zou, Guangyong"],"dc:creator":["Lu, Grace"],"dc:date.accessioned":["2025-07-10T19:19:35Z"],"dc:date.available":["2025-07-10T19:19:35Z"],"dc:date.issued":["2021-06-01"],"dc:description":["Collaborative Specialization: Biostatistics","The thesis cover page in the PDF document includes references to Western University’s previous institutional repository platform, known as Scholarship@Western, and links to that platform (beginning with ir.lib.uwo.ca). In citing or referring to this thesis, use the DOI or handle from this page instead. Sample citation: Author name, \"Thesis title.\" (Year). Western University Open Repository. https://doi.org/10.71858/123456."],"dc:description.abstract":["The area under the receiver operating characteristic curve (AUC) is commonly used to quantify the discriminative ability of tests with ordinal or continuous test data. When planning a study to evaluate a new test, it is important to determine a minimum sample size required to achieve a prespecified precision of estimating AUC. However, conventional sample size formulas do not consider the probability of achieving a prespecified precision, resulting in underestimation of sample sizes. To incorporate the assurance probability, asymptotic sample size formulas were derived using different variance estimators for AUC in this thesis. The precision of AUC estimations was quantified by either lower confidence limits or interval width. The performance of proposed sample size formulas was evaluated through simulation studies. Simulation results show that the formula based on lower limits with the nonparametric method performs best and can be used with both ordinal and continuous data. The methods are illustrated with examples from previously published data."],"dc:identifier.uri":["https://hdl.handle.net/20.500.14721/31457"],"dc:language.iso":["en_ca"],"dc:publisher":["The University of Western Ontario"],"dc:subject":["assurance probability","confidence interval","variance"],"dc:title":["Sample Size Formulas For Estimating Areas Under the Receiver Operating Characteristic Curves With Precision and Assurance"],"dc:type":["thesis"],"thesis:degree_discipline":["Epidemiology and Biostatistics"],"thesis:degree_name":["M Sc"]},"updated_at":"2026-07-27T21:56:09Z"}