TY - GEN
T1 - Bayesian applications to longitudinal analysis on medical data with discrete outcomes
AU - Juan, Li
AU - Wei, Zhu
AU - Xuena, Wang
AU - De Santi, Susan
AU - De Leon, Mony J.
PY - 2005
Y1 - 2005
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/33846897784
M3 - Conference contribution
AN - SCOPUS:33846897784
SN - 0780387406
SN - 9780780387409
T3 - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
SP - 1204
EP - 1207
BT - Proceedings of the 2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005
T2 - 2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005
Y2 - 1 September 2005 through 4 September 2005
ER -