{"id":{"repo_id":"ohiolink","oai_identifier":"oai:etd.ohiolink.edu:case1354508776"},"canonical_url":"https://search.dev.ndltd.org/etd/ohiolink/oai:etd.ohiolink.edu:case1354508776","repository":{"repo_id":"ohiolink","name":"OhioLINK","base_url":"https://etd.ohiolink.edu/acprod/odb_etd/ws/oai/oai"},"display":{"title":"JOINT MODELING OF MULTIVARIATE LONGITUDINAL DATA AND COMPETING RISKS DATA","abstract":"In many clinical studies that involve follow-up, it is common to observe one or more sequences of longitudinal measurements, as well as one or more time to event outcomes. A competing risks situation arises when the probability of occurrence of one event isaltered/hindered by another time to event. A classical example is different causes of death. When the missing data mechanism in the longitudinal process is non-ignorable due to informative dropout, one has to jointly model the longitudinal data and the timeto event outcomes in order to obtain valid inferences. Recently, there has been much attention paid to the joint analysis of single longitudinal measurements and single time to event data. A natural extension in the joint modeling literature is to the case whichinvolves a joint analysis of multiple failure types and longitudinal process(es). However, to the best of our knowledge only a few such extensions currently exist in the literature.We, in this thesis, propose one such extension where multiple longitudinal responses are jointly modeled with competing-risks time to event data (multiple failure types). Our joint model consists of two sub-models: a system of non-linear mixed effects sub-models for the multiple longitudinal responses; and a system of cause-specific hazards frailty submodels for competing risks, with associations among multiple longitudinal responses andmultiple failure types (competing risks) are modeled using latent parameters. That is, we use the shared parameter modeling approach in this joint model. The joint model is applied to a data set of patients with end-stage heart failure awaiting heart transplant.Heart failure occurs when heart loses the ability to pump enough blood through the body. Currently, most of the patients with end-stage heart failure who are eligible for heart transplant are inserted with a mechanical circulatory support (MCS) devices (suchas left ventricular assists devices) to support the failing heart while waiting for heart transplant. While on the MCS, their liver and renal functions may worsen and these in turn may influence one of the two possible outcomes: i. death before transplant; ii.transplant. These two events can be considered as competing risks. We use longitudinal measurements of bilirubin as a marker for liver function and longitudinal measurements of GFR as a marker for renal function. By using our joint model we assess the effect(association) of liver and renal function on the competing risks.","abstract_html":"In many clinical studies that involve follow-up, it is common to observe one or more sequences of longitudinal measurements, as well as one or more time to event outcomes. A competing risks situation arises when the probability of occurrence of one event isaltered/hindered by another time to event. A classical example is different causes of death. When the missing data mechanism in the longitudinal process is non-ignorable due to informative dropout, one has to jointly model the longitudinal data and the timeto event outcomes in order to obtain valid inferences. Recently, there has been much attention paid to the joint analysis of single longitudinal measurements and single time to event data. A natural extension in the joint modeling literature is to the case whichinvolves a joint analysis of multiple failure types and longitudinal process(es). However, to the best of our knowledge only a few such extensions currently exist in the literature.We, in this thesis, propose one such extension where multiple longitudinal responses are jointly modeled with competing-risks time to event data (multiple failure types). Our joint model consists of two sub-models: a system of non-linear mixed effects sub-models for the multiple longitudinal responses; and a system of cause-specific hazards frailty submodels for competing risks, with associations among multiple longitudinal responses andmultiple failure types (competing risks) are modeled using latent parameters. That is, we use the shared parameter modeling approach in this joint model. The joint model is applied to a data set of patients with end-stage heart failure awaiting heart transplant.Heart failure occurs when heart loses the ability to pump enough blood through the body. Currently, most of the patients with end-stage heart failure who are eligible for heart transplant are inserted with a mechanical circulatory support (MCS) devices (suchas left ventricular assists devices) to support the failing heart while waiting for heart transplant. While on the MCS, their liver and renal functions may worsen and these in turn may influence one of the two possible outcomes: i. death before transplant; ii.transplant. These two events can be considered as competing risks. We use longitudinal measurements of bilirubin as a marker for liver function and longitudinal measurements of GFR as a marker for renal function. By using our joint model we assess the effect(association) of liver and renal function on the competing risks.","abstract_has_math":false,"creators":["Rajeswaran, Jeevanantham"],"institution":"Case Western Reserve University School of Graduate Studies","degree_name":"Doctor of Philosophy","degree_level":"doctoral","degree_discipline":"Epidemiology and Biostatistics","degree_department":null,"school":null,"contributors":["Schluchter, Mark"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2013,"date_issued":"2013-03-08","date_published":"2013-03-08","updated_at":"2026-07-24T03:35:52Z","subjects":["Biostatistics","Joint Modeling","multivariate longitudinal data","competing risks data","non-linear mixed effect model","multi-phase models"],"languages":["English"],"rights":["unrestricted","This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://rave.ohiolink.edu/etdc/view?acc_num=case1354508776","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Schluchter, Mark"]},{"key":"dc:creator","label":"Author","values":["Rajeswaran, Jeevanantham"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2013-03-08"]},{"key":"dc:publisher","label":"Institution","values":["Case Western Reserve University School of Graduate Studies / OhioLINK"]},{"key":"dc:type","label":"Dc Type","values":["Electronic Thesis or Dissertation"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Epidemiology and Biostatistics"]},{"key":"thesis:degree_level","label":"Degree Level","values":["doctoral"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Case Western Reserve University School of Graduate Studies"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Biostatistics","Joint Modeling","multivariate longitudinal data","competing risks data","non-linear mixed effect model","multi-phase models"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English"]},{"key":"dc:rights","label":"Dc Rights","values":["unrestricted","This thesis or dissertation is protected by copyright: all rights reserved. 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Recently, there has been much attention paid to the joint analysis of single longitudinal measurements and single time to event data. A natural extension in the joint modeling literature is to the case whichinvolves a joint analysis of multiple failure types and longitudinal process(es). However, to the best of our knowledge only a few such extensions currently exist in the literature.We, in this thesis, propose one such extension where multiple longitudinal responses are jointly modeled with competing-risks time to event data (multiple failure types). Our joint model consists of two sub-models: a system of non-linear mixed effects sub-models for the multiple longitudinal responses; and a system of cause-specific hazards frailty submodels for competing risks, with associations among multiple longitudinal responses andmultiple failure types (competing risks) are modeled using latent parameters. That is, we use the shared parameter modeling approach in this joint model. The joint model is applied to a data set of patients with end-stage heart failure awaiting heart transplant.Heart failure occurs when heart loses the ability to pump enough blood through the body. Currently, most of the patients with end-stage heart failure who are eligible for heart transplant are inserted with a mechanical circulatory support (MCS) devices (suchas left ventricular assists devices) to support the failing heart while waiting for heart transplant. While on the MCS, their liver and renal functions may worsen and these in turn may influence one of the two possible outcomes: i. death before transplant; ii.transplant. These two events can be considered as competing risks. We use longitudinal measurements of bilirubin as a marker for liver function and longitudinal measurements of GFR as a marker for renal function. By using our joint model we assess the effect(association) of liver and renal function on the competing risks."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf","p.170","6.14 MB"]},{"key":"dc:title","label":"Title","values":["JOINT MODELING OF MULTIVARIATE LONGITUDINAL DATA AND COMPETING RISKS DATA"]}]}],"canonical_facts":{"dc:contributor":["Schluchter, Mark"],"dc:creator":["Rajeswaran, Jeevanantham"],"dc:date":["2013-03-08"],"dc:description":["In many clinical studies that involve follow-up, it is common to observe one or more sequences of longitudinal measurements, as well as one or more time to event outcomes. A competing risks situation arises when the probability of occurrence of one event isaltered/hindered by another time to event. A classical example is different causes of death. When the missing data mechanism in the longitudinal process is non-ignorable due to informative dropout, one has to jointly model the longitudinal data and the timeto event outcomes in order to obtain valid inferences. Recently, there has been much attention paid to the joint analysis of single longitudinal measurements and single time to event data. A natural extension in the joint modeling literature is to the case whichinvolves a joint analysis of multiple failure types and longitudinal process(es). However, to the best of our knowledge only a few such extensions currently exist in the literature.We, in this thesis, propose one such extension where multiple longitudinal responses are jointly modeled with competing-risks time to event data (multiple failure types). Our joint model consists of two sub-models: a system of non-linear mixed effects sub-models for the multiple longitudinal responses; and a system of cause-specific hazards frailty submodels for competing risks, with associations among multiple longitudinal responses andmultiple failure types (competing risks) are modeled using latent parameters. That is, we use the shared parameter modeling approach in this joint model. The joint model is applied to a data set of patients with end-stage heart failure awaiting heart transplant.Heart failure occurs when heart loses the ability to pump enough blood through the body. Currently, most of the patients with end-stage heart failure who are eligible for heart transplant are inserted with a mechanical circulatory support (MCS) devices (suchas left ventricular assists devices) to support the failing heart while waiting for heart transplant. While on the MCS, their liver and renal functions may worsen and these in turn may influence one of the two possible outcomes: i. death before transplant; ii.transplant. These two events can be considered as competing risks. We use longitudinal measurements of bilirubin as a marker for liver function and longitudinal measurements of GFR as a marker for renal function. By using our joint model we assess the effect(association) of liver and renal function on the competing risks."],"dc:format":["application/pdf","p.170","6.14 MB"],"dc:identifier":["http://rave.ohiolink.edu/etdc/view?acc_num=case1354508776"],"dc:language":["English"],"dc:publisher":["Case Western Reserve University School of Graduate Studies / OhioLINK"],"dc:rights":["unrestricted","This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws."],"dc:subject":["Biostatistics","Joint Modeling","multivariate longitudinal data","competing risks data","non-linear mixed effect model","multi-phase models"],"dc:title":["JOINT MODELING OF MULTIVARIATE LONGITUDINAL DATA AND COMPETING RISKS DATA"],"dc:type":["Electronic Thesis or Dissertation"],"thesis:degree_discipline":["Epidemiology and Biostatistics"],"thesis:degree_level":["doctoral"],"thesis:degree_name":["Doctor of Philosophy"],"thesis:institution_name":["Case Western Reserve University School of Graduate Studies"]},"updated_at":"2026-07-24T03:35:52Z"}