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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Juan Li Wei Zhu Xuena Wang De Santi, S. de Leon, M.J. |
| Copyright Year | 2005 |
| Description | Author affiliation: Dept. of Psychiatry, New York Univ., NY (Juan Li) |
| Abstract | Many prediction studies of medical research lead to discrete longitudinal data with repeated measurement and categorical outcomes. Therefore the traditional likelihood-based methods for continuous outcome measures are no longer suitable. With the development of modern computing technologies and improved scope for estimation via iterative sampling methods, Bayesian analysis is becoming increasingly popular among biostatisticians. Markov chain Monte Carlo (MCMC), for the implementation of Bayesian methods has rendered the implementation of complex Bayesian models a reality. In addition, the availability of software like WinBUGS has made the utilization of MCMC straightforward. In this study, we developed a full Bayesian version of generalized linear models for binary longitudinal data and applied it to a longitudinal prediction study of Alzheimer's disease conducted at New York University School of Medicine |
| Starting Page | 1204 |
| Ending Page | 1207 |
| File Size | 302687 |
| Page Count | 4 |
| File Format | |
| ISBN | 0780387414 |
| DOI | 10.1109/IEMBS.2005.1616640 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2006-01-17 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Bayesian methods Data analysis Predictive models Alzheimer's disease Biomedical engineering Autocorrelation Medical diagnostic imaging Hippocampus Biomarkers Head |
| Content Type | Text |
| Resource Type | Article |
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