{"id":{"repo_id":"uwo","oai_identifier":"oai:uwo.scholaris.ca:20.500.14721/34543"},"canonical_url":"https://search.dev.ndltd.org/etd/uwo/oai:uwo.scholaris.ca:20.500.14721/34543","repository":{"repo_id":"uwo","name":"Western University","base_url":"https://uwo.scholaris.ca/server/oai/request"},"display":{"title":"Confidence Interval Estimation of Cumulative Incidence for Clustered Competing Risks","abstract":"In a cluster randomized trial studying a primary outcome patients are sometimes exposed to competing events. These are risks that alter the probability of the primary outcome occurring. Traditional methods of estimating the cumulative incidence for an outcome and its associated confidence interval under competing risks do not account for the effect of clustering. This may cause incorrect estimation of confidence intervals because outcomes among patients from the same center are correlated. This thesis compared six nonparametric methods of confidence interval construction for cumulative incidence, four of which account for clustering effect, under competing risks via simulation study. Over the range of examined scenarios, if the clustering effect is mild (i.e. ICC = 0.01), estimators not accounting for clustering never have worse coverage than those that do. However, in cases with a large clustering effect (i.e. ICC = 0.05), using confidence interval estimators accounting for clustering should be considered.","abstract_html":"In a cluster randomized trial studying a primary outcome patients are sometimes exposed to competing events. These are risks that alter the probability of the primary outcome occurring. Traditional methods of estimating the cumulative incidence for an outcome and its associated confidence interval under competing risks do not account for the effect of clustering. This may cause incorrect estimation of confidence intervals because outcomes among patients from the same center are correlated. This thesis compared six nonparametric methods of confidence interval construction for cumulative incidence, four of which account for clustering effect, under competing risks via simulation study. Over the range of examined scenarios, if the clustering effect is mild (i.e. ICC = 0.01), estimators not accounting for clustering never have worse coverage than those that do. However, in cases with a large clustering effect (i.e. ICC = 0.05), using confidence interval estimators accounting for clustering should be considered.","abstract_has_math":false,"creators":["Sivaswamy, Atul"],"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":["Neil Klar","Merrick Zwarenstien"],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017-02-17","date_published":"2017-02-17","updated_at":"2026-07-27T21:56:09Z","subjects":["Competing Risks","Clustering","Cumulative Incidence Function."],"languages":["en_ca"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/20.500.14721/34543","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Neil Klar","Merrick Zwarenstien"]},{"key":"dc:creator","label":"Author","values":["Sivaswamy, Atul"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-07-10T20:05:15Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-07-10T20:05:15Z"]},{"key":"dc:date.issued","label":"Date","values":["2017-02-17"]},{"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":["Competing Risks","Clustering","Cumulative Incidence Function."]}]},{"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/20.500.14721/34543"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["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":["In a cluster randomized trial studying a primary outcome patients are sometimes exposed to competing events. These are risks that alter the probability of the primary outcome occurring. Traditional methods of estimating the cumulative incidence for an outcome and its associated confidence interval under competing risks do not account for the effect of clustering. This may cause incorrect estimation of confidence intervals because outcomes among patients from the same center are correlated. This thesis compared six nonparametric methods of confidence interval construction for cumulative incidence, four of which account for clustering effect, under competing risks via simulation study. Over the range of examined scenarios, if the clustering effect is mild (i.e. ICC = 0.01), estimators not accounting for clustering never have worse coverage than those that do. However, in cases with a large clustering effect (i.e. ICC = 0.05), using confidence interval estimators accounting for clustering should be considered."]},{"key":"dc:title","label":"Title","values":["Confidence Interval Estimation of Cumulative Incidence for Clustered Competing Risks"]}]}],"canonical_facts":{"dc:contributor.advisor":["Neil Klar","Merrick Zwarenstien"],"dc:creator":["Sivaswamy, Atul"],"dc:date.accessioned":["2025-07-10T20:05:15Z"],"dc:date.available":["2025-07-10T20:05:15Z"],"dc:date.issued":["2017-02-17"],"dc:description":["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":["In a cluster randomized trial studying a primary outcome patients are sometimes exposed to competing events. These are risks that alter the probability of the primary outcome occurring. Traditional methods of estimating the cumulative incidence for an outcome and its associated confidence interval under competing risks do not account for the effect of clustering. This may cause incorrect estimation of confidence intervals because outcomes among patients from the same center are correlated. This thesis compared six nonparametric methods of confidence interval construction for cumulative incidence, four of which account for clustering effect, under competing risks via simulation study. Over the range of examined scenarios, if the clustering effect is mild (i.e. ICC = 0.01), estimators not accounting for clustering never have worse coverage than those that do. However, in cases with a large clustering effect (i.e. ICC = 0.05), using confidence interval estimators accounting for clustering should be considered."],"dc:identifier.uri":["https://hdl.handle.net/20.500.14721/34543"],"dc:language.iso":["en_ca"],"dc:publisher":["The University of Western Ontario"],"dc:subject":["Competing Risks","Clustering","Cumulative Incidence Function."],"dc:title":["Confidence Interval Estimation of Cumulative Incidence for Clustered Competing Risks"],"dc:type":["thesis"],"thesis:degree_discipline":["Epidemiology and Biostatistics"],"thesis:degree_name":["M Sc"]},"updated_at":"2026-07-27T21:56:09Z"}