In clinical trials, time-to-event data (survival component) and longitudinal data (longitudinal component) are often collected. In order to model these two components simultaneously, the joint modeling approach which can reduce potential biases and improve the efficiency in estimating treatment effects becomes increasingly important. In this work, we proposed a joint model where a nonlinear mixed-effect model with both exponential shrinkage and a linear progression term with time is used to describe the change in tumor size and an accelerated failure time frailty model with either weibull or exponential distribution is used to describe the overall survival time. The survival and longitudinal components are linked through random effects with appropriate adjustments. The simulation study shows that the joint analysis is better than separate analysis in terms of the parameter estimate and goodness-of-fit. A real dataset is applied to our proposed model to see the applicability. The Weibull survival distribution is found to have a better fit than exponential survival distribution in term of the goodness-of-fit in this real dataset.
","abstract_html":"<p>In clinical trials, time-to-event data (survival component) and longitudinal data (longitudinal component) are often collected. In order to model these two components simultaneously, the joint modeling approach which can reduce potential biases and improve the efficiency in estimating treatment effects becomes increasingly important. In this work, we proposed a joint model where a nonlinear mixed-effect model with both exponential shrinkage and a linear progression term with time is used to describe the change in tumor size and an accelerated failure time frailty model with either weibull or exponential distribution is used to describe the overall survival time. The survival and longitudinal components are linked through random effects with appropriate adjustments. The simulation study shows that the joint analysis is better than separate analysis in terms of the parameter estimate and goodness-of-fit. A real dataset is applied to our proposed model to see the applicability. The Weibull survival distribution is found to have a better fit than exponential survival distribution in term of the goodness-of-fit in this real dataset.</p>","abstract_has_math":false,"creators":["Bao, Weichao"],"institution":null,"degree_name":"Ph.D.","degree_level":"Campus Access Dissertation","degree_discipline":"Epidemiology and Biostatistics","degree_department":null,"school":null,"contributors":["Bo Cai"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2012,"date_issued":"2012-01-01T08:00:00Z","date_published":"2012-01-01T08:00:00Z","updated_at":"2026-07-24T04:38:07Z","subjects":["Biostatistics","Physical Sciences and Mathematics","Statistics and Probability"],"languages":[],"rights":["© 2012, Weichao Bao"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarcommons.sc.edu/etd/542","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Bo Cai"]},{"key":"dc:creator","label":"Author","values":["Bao, Weichao"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Epidemiology and Biostatistics"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Campus Access Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Biostatistics","Physical Sciences and Mathematics","Statistics and Probability"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["© 2012, Weichao Bao"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarcommons.sc.edu/etd/542"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["In clinical trials, time-to-event data (survival component) and longitudinal data (longitudinal component) are often collected. In order to model these two components simultaneously, the joint modeling approach which can reduce potential biases and improve the efficiency in estimating treatment effects becomes increasingly important. In this work, we proposed a joint model where a nonlinear mixed-effect model with both exponential shrinkage and a linear progression term with time is used to describe the change in tumor size and an accelerated failure time frailty model with either weibull or exponential distribution is used to describe the overall survival time. The survival and longitudinal components are linked through random effects with appropriate adjustments. The simulation study shows that the joint analysis is better than separate analysis in terms of the parameter estimate and goodness-of-fit. A real dataset is applied to our proposed model to see the applicability. The Weibull survival distribution is found to have a better fit than exponential survival distribution in term of the goodness-of-fit in this real dataset.
"]},{"key":"dc:title","label":"Title","values":["Joint Modeling of Tumor Size and Time-to-Event"]}]}],"canonical_facts":{"dc:contributor":["Bo Cai"],"dc:creator":["Bao, Weichao"],"dc:description.abstract":["In clinical trials, time-to-event data (survival component) and longitudinal data (longitudinal component) are often collected. In order to model these two components simultaneously, the joint modeling approach which can reduce potential biases and improve the efficiency in estimating treatment effects becomes increasingly important. In this work, we proposed a joint model where a nonlinear mixed-effect model with both exponential shrinkage and a linear progression term with time is used to describe the change in tumor size and an accelerated failure time frailty model with either weibull or exponential distribution is used to describe the overall survival time. The survival and longitudinal components are linked through random effects with appropriate adjustments. The simulation study shows that the joint analysis is better than separate analysis in terms of the parameter estimate and goodness-of-fit. A real dataset is applied to our proposed model to see the applicability. The Weibull survival distribution is found to have a better fit than exponential survival distribution in term of the goodness-of-fit in this real dataset.
"],"dc:identifier":["https://scholarcommons.sc.edu/etd/542"],"dc:rights":["© 2012, Weichao Bao"],"dc:subject":["Biostatistics","Physical Sciences and Mathematics","Statistics and Probability"],"dc:title":["Joint Modeling of Tumor Size and Time-to-Event"],"thesis:degree_discipline":["Epidemiology and Biostatistics"],"thesis:degree_level":["Campus Access Dissertation"],"thesis:degree_name":["Ph.D."]},"updated_at":"2026-07-24T04:38:07Z"}