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Please use this identifier to cite or link to this item:
http://ntur.lib.ntu.edu.tw/handle/246246/171225
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| Title: | A Markov Regression Random-Effects Model for Remission of Functional Disability in Patients Following a First Stroke: A Bayesian Approach |
| Authors: | 潘信良;吳惠敏;嚴明芳;陳秀熙
PAN, SHIN-LIANG;WU, HUI-MIN;YEN, AMY MING-FANG;CHEN, TONY HSIU-HIS |
| Contributors: | 復健部 |
| Date: | 2007 |
| Issue Date: | 2009-10-21T07:19:36Z |
| Abstract: | Few attempts have been made to model the dynamics of stroke- related disability. It is possible though, using panel data and multi-state Markov regression models that incorporate measured covariates and latent variables (random effects). This study aimed to model a series of functional transitions (following a first stroke) using a three-state Markov model with or without considering random effects. Several proportional hazards parameterizations were considered. A Bayesian approach that utilizes the Markov Chain Monte Carlo (MCMC) and Gibbs sampling functionality of WinBUGS (a Windows-based Bayesian software package) was developed to generate the marginal posterior distributions of the various transition parameters (e.g. the transition rates and transition probabilities). Model building and comparisons was guided by reference to the deviance information criteria (DIC). Of the four proportional hazards models considered, exponential regression was preferred because it led to the smallest deviances. Adding random effects further improved the model fit. Of the covariates considered, only age, infarct size, and baseline functional status were significant. By using our final model we were able to make individual predictions about functional recovery in stroke patients. Copyright (c) 2007 John Wiley & Sons, Ltd. |
| Relation: | STATISTICS IN MEDICINE v.26 n.29 pp.5335-5353 |
| Appears in Collections: | [附設醫院復健部] 期刊論文
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