In longitudinal studies, a popular model is the linear mixed model that includes fixed effects and subject specific random effects. In many clinical trials and other medical and reliability studies, we can often obtain repeated measurements or longitudinal data that includes survival or time-to-event histories. Recently, methods for jointly modeling longitudinal and survival data have gained popularity in the statistical literature. In this dissertation, we consider the problem of variable selection in a joint modeling framework where longitudinal and survival data are modeled jointly. Dirichlet process priors are used to relax the parametric assumption of random effects, which has advantages of making the model more robust against possible misspecifications and allows the clustering of subjects. A fully Bayesian method for subset selection of fixed and random effects in joint models is proposed. Simulation examples and an application are used for method evaluation and illustration.
","abstract_html":"<p>In longitudinal studies, a popular model is the linear mixed model that includes fixed effects and subject specific random effects. In many clinical trials and other medical and reliability studies, we can often obtain repeated measurements or longitudinal data that includes survival or time-to-event histories. Recently, methods for jointly modeling longitudinal and survival data have gained popularity in the statistical literature. In this dissertation, we consider the problem of variable selection in a joint modeling framework where longitudinal and survival data are modeled jointly. Dirichlet process priors are used to relax the parametric assumption of random effects, which has advantages of making the model more robust against possible misspecifications and allows the clustering of subjects. A fully Bayesian method for subset selection of fixed and random effects in joint models is proposed. Simulation examples and an application are used for method evaluation and illustration.</p>","abstract_has_math":false,"creators":["Bao, Haikun"],"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":2011,"date_issued":"2011-01-01T08:00:00Z","date_published":"2011-01-01T08:00:00Z","updated_at":"2026-07-24T04:37:56Z","subjects":["Biostatistics","Physical Sciences and Mathematics","Statistics and Probability"],"languages":[],"rights":["© 2011, Haikun Bao"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarcommons.sc.edu/etd/541","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, Haikun"]}]},{"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":["© 2011, Haikun Bao"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarcommons.sc.edu/etd/541"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["In longitudinal studies, a popular model is the linear mixed model that includes fixed effects and subject specific random effects. In many clinical trials and other medical and reliability studies, we can often obtain repeated measurements or longitudinal data that includes survival or time-to-event histories. Recently, methods for jointly modeling longitudinal and survival data have gained popularity in the statistical literature. In this dissertation, we consider the problem of variable selection in a joint modeling framework where longitudinal and survival data are modeled jointly. Dirichlet process priors are used to relax the parametric assumption of random effects, which has advantages of making the model more robust against possible misspecifications and allows the clustering of subjects. A fully Bayesian method for subset selection of fixed and random effects in joint models is proposed. Simulation examples and an application are used for method evaluation and illustration.
"]},{"key":"dc:title","label":"Title","values":["Semiparametric Bayesian Joint Model With Variable Selection"]}]}],"canonical_facts":{"dc:contributor":["Bo Cai"],"dc:creator":["Bao, Haikun"],"dc:description.abstract":["In longitudinal studies, a popular model is the linear mixed model that includes fixed effects and subject specific random effects. In many clinical trials and other medical and reliability studies, we can often obtain repeated measurements or longitudinal data that includes survival or time-to-event histories. Recently, methods for jointly modeling longitudinal and survival data have gained popularity in the statistical literature. In this dissertation, we consider the problem of variable selection in a joint modeling framework where longitudinal and survival data are modeled jointly. Dirichlet process priors are used to relax the parametric assumption of random effects, which has advantages of making the model more robust against possible misspecifications and allows the clustering of subjects. A fully Bayesian method for subset selection of fixed and random effects in joint models is proposed. Simulation examples and an application are used for method evaluation and illustration.
"],"dc:identifier":["https://scholarcommons.sc.edu/etd/541"],"dc:rights":["© 2011, Haikun Bao"],"dc:subject":["Biostatistics","Physical Sciences and Mathematics","Statistics and Probability"],"dc:title":["Semiparametric Bayesian Joint Model With Variable Selection"],"thesis:degree_discipline":["Epidemiology and Biostatistics"],"thesis:degree_level":["Campus Access Dissertation"],"thesis:degree_name":["Ph.D."]},"updated_at":"2026-07-24T04:37:56Z"}