Joint Models for Multiple Longitudinal Processes and Time-to-event Outcome
dc.contributor.author | Yang, Lili | |
dc.contributor.author | Yu, Menggang | |
dc.contributor.author | Gao, Sujuan | |
dc.contributor.department | Department of Biostatistics, Richard M. Fairbanks School of Public Health | en_US |
dc.date.accessioned | 2016-12-09T19:03:21Z | |
dc.date.available | 2016-12-09T19:03:21Z | |
dc.date.issued | 2016 | |
dc.description.abstract | Joint models are statistical tools for estimating the association between time-to-event and longitudinal outcomes. One challenge to the application of joint models is its computational complexity. Common estimation methods for joint models include a two-stage method, Bayesian and maximum-likelihood methods. In this work, we consider joint models of a time-to-event outcome and multiple longitudinal processes and develop a maximum-likelihood estimation method using the expectation–maximization algorithm. We assess the performance of the proposed method via simulations and apply the methodology to a data set to determine the association between longitudinal systolic and diastolic blood pressure measures and time to coronary artery disease. | en_US |
dc.eprint.version | Author's manuscript | en_US |
dc.identifier.citation | Yang, L., Yu, M., & Gao, S. (2016). Joint models for multiple longitudinal processes and time-to-event outcome. Journal of Statistical Computation and Simulation, 86(18), 3682–3700. https://doi.org/10.1080/00949655.2016.1181760 | en_US |
dc.identifier.uri | https://hdl.handle.net/1805/11600 | |
dc.language.iso | en | en_US |
dc.publisher | Taylor & Francis | en_US |
dc.relation.isversionof | 10.1080/00949655.2016.1181760 | en_US |
dc.relation.journal | Journal of Statistical Computation and Simulation | en_US |
dc.rights | Publisher Policy | en_US |
dc.source | Author | en_US |
dc.subject | joint models | en_US |
dc.subject | EM algorithm | en_US |
dc.subject | simulation | en_US |
dc.title | Joint Models for Multiple Longitudinal Processes and Time-to-event Outcome | en_US |
dc.type | Article | en_US |