{"id":{"repo_id":"ottawa-retro","oai_identifier":"oai:ruor.uottawa.ca:10393/32235"},"canonical_url":"https://search.dev.ndltd.org/etd/ottawa-retro/oai:ruor.uottawa.ca:10393/32235","repository":{"repo_id":"ottawa-retro","name":"University of Ottawa","base_url":"https://ruor.uottawa.ca/server/oai/request"},"display":{"title":"Statistical Analysis of Treatment Compliance for Clinical Trials using Electronic Compliance Monitoring","abstract":"Compliance, the extent to which patients follow a medication regimen, has been recognized as one of the most serious problems facing medical practice today. Recent developments in assessing compliance include electronic compliance monitors (ECM), devices that record the date and time of the release of medication from its original container. This allows utilizing ECM compliance data in statistical analyses related to clinical trials. This thesis proposes ways of dealing with the time-varying nature of compliance. We examine the compliance behaviour from real ECM data through statistical analysis of compliance rate, followed by a time-to-event analysis with respect to first noncompliance event. Then, using discrete event simulation and proportional hazards models we compare analyses using a fixed treatment covariate and time-varying compliance covariate based on pharmacokinetic principles in estimating treatment effect. We observe a reduction of up to 40% in EMSE in favour of the latter model for treatment effect estimation.","abstract_html":"Compliance, the extent to which patients follow a medication regimen, has been recognized as one of the most serious problems facing medical practice today. Recent developments in assessing compliance include electronic compliance monitors (ECM), devices that record the date and time of the release of medication from its original container. This allows utilizing ECM compliance data in statistical analyses related to clinical trials. This thesis proposes ways of dealing with the time-varying nature of compliance. We examine the compliance behaviour from real ECM data through statistical analysis of compliance rate, followed by a time-to-event analysis with respect to first noncompliance event. Then, using discrete event simulation and proportional hazards models we compare analyses using a fixed treatment covariate and time-varying compliance covariate based on pharmacokinetic principles in estimating treatment effect. We observe a reduction of up to 40% in EMSE in favour of the latter model for treatment effect estimation.","abstract_has_math":false,"creators":["Sirois, Jean-Karl"],"institution":"Université d'Ottawa / University of Ottawa","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Bergeron, Pierre-Jérôme","Alvo, Mayer"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015-04-15T18:24:24Z","date_published":"2015-04-15T18:24:24Z","updated_at":"2026-07-24T03:39:29Z","subjects":["Compliance","Pharmacokinetic","Treatment Effect","Discrete Event Simulation"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["http://dx.doi.org/10.20381/ruor-3900"],"render_values":[{"text":"http://dx.doi.org/10.20381/ruor-3900","href":"http://dx.doi.org/10.20381/ruor-3900","code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/10393/32235","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Bergeron, Pierre-Jérôme","Alvo, Mayer"]},{"key":"dc:creator","label":"Author","values":["Sirois, Jean-Karl"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2015-04-15T18:24:24Z","2015"]},{"key":"dc:publisher","label":"Institution","values":["Université d'Ottawa / University of Ottawa"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Compliance","Pharmacokinetic","Treatment Effect","Discrete Event Simulation"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/10393/32235","http://dx.doi.org/10.20381/ruor-3900"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Compliance, the extent to which patients follow a medication regimen, has been recognized as one of the most serious problems facing medical practice today. Recent developments in assessing compliance include electronic compliance monitors (ECM), devices that record the date and time of the release of medication from its original container. This allows utilizing ECM compliance data in statistical analyses related to clinical trials. This thesis proposes ways of dealing with the time-varying nature of compliance. We examine the compliance behaviour from real ECM data through statistical analysis of compliance rate, followed by a time-to-event analysis with respect to first noncompliance event. Then, using discrete event simulation and proportional hazards models we compare analyses using a fixed treatment covariate and time-varying compliance covariate based on pharmacokinetic principles in estimating treatment effect. We observe a reduction of up to 40% in EMSE in favour of the latter model for treatment effect estimation."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Statistical Analysis of Treatment Compliance for Clinical Trials using Electronic Compliance Monitoring"]}]}],"canonical_facts":{"dc:contributor":["Bergeron, Pierre-Jérôme","Alvo, Mayer"],"dc:creator":["Sirois, Jean-Karl"],"dc:date":["2015-04-15T18:24:24Z","2015"],"dc:description":["Compliance, the extent to which patients follow a medication regimen, has been recognized as one of the most serious problems facing medical practice today. Recent developments in assessing compliance include electronic compliance monitors (ECM), devices that record the date and time of the release of medication from its original container. This allows utilizing ECM compliance data in statistical analyses related to clinical trials. This thesis proposes ways of dealing with the time-varying nature of compliance. We examine the compliance behaviour from real ECM data through statistical analysis of compliance rate, followed by a time-to-event analysis with respect to first noncompliance event. Then, using discrete event simulation and proportional hazards models we compare analyses using a fixed treatment covariate and time-varying compliance covariate based on pharmacokinetic principles in estimating treatment effect. We observe a reduction of up to 40% in EMSE in favour of the latter model for treatment effect estimation."],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/10393/32235","http://dx.doi.org/10.20381/ruor-3900"],"dc:language":["en"],"dc:publisher":["Université d'Ottawa / University of Ottawa"],"dc:subject":["Compliance","Pharmacokinetic","Treatment Effect","Discrete Event Simulation"],"dc:title":["Statistical Analysis of Treatment Compliance for Clinical Trials using Electronic Compliance Monitoring"],"dc:type":["Thesis"]},"updated_at":"2026-07-24T03:39:29Z"}