https://scholars.lib.ntu.edu.tw/handle/123456789/519188
標題: | Bayesian random-effect model for predicting outcome fraught with heterogeneity: An illustration with episodes of 44 patients with intractable epilepsy | 作者: | Yen A.M.-F. HORNG-HUEI LIOU Lin H.-L. Chen, Tony Hsiu Hsi |
關鍵字: | Bayesian acyclic graphic model; Heterogeneity; Markov Chain Monte Carlo (MCMC); Predictive model; Random effect | 公開日期: | 2006 | 出版社: | Schattauer GmbH | 卷: | 45 | 期: | 6 | 起(迄)頁: | 631-637 | 來源出版物: | Methods of Information in Medicine | 摘要: | Objective: The study aimed to develop a predictive model to deal with data fraught with heterogeneity that cannot be explained by sampling variation or measured covariates. Methods: The random-effect Poisson regression model was first proposed to deal with over-dispersion for data fraught with heterogeneity offer making allowance for measured covariates. Bayesian acyclic graphic model in conjunction with Markov Chain Monte Carlo (MCMC) technique was then applied to estimate the parameters of both relevant covariates and random effect. Predictive distribution was then generated to compare the predicted with the observed for the Bayesian model with and without random effect. Data from repeated measurement of episodes among 44 patients with intractable epilepsy were used as an illustration. Results: The application of Poisson regression without taking heterogeneity into account to epilepsy data yielded a large value of heterogeneity (heterogeneity factor = 17.90, deviance = 1485, degree of freedom (df) = 83). After taking the random effect into account, the value of heterogeneity factor was greatly reduced (heterogeneity factor = 0.52, deviance = 42.5, df = 81). The Pearson χ2 for the comparison between the expected seizure frequencies and the observed ones at two and three months of the model with and without random effect were 34.27 (p = 1.00) and 1799.90 (p < 0.0001), respectively. Conclusion: The Bayesian acyclic model using the MCMC method was demonstrated to have great potential for disease prediction while data show over-dispersion attributed either to correlated property or to subject-to-subject variability. ? 2006 Schattauer GmbH. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-33845917898&doi=10.1055%2fs-0038-1634127&partnerID=40&md5=45ec7b890f2dbb567badd23e8fdf6dfc https://scholars.lib.ntu.edu.tw/handle/123456789/519188 |
DOI: | 10.1055/s-0038-1634127 | SDG/關鍵字: | adolescent; adult; article; Bayes theorem; clinical article; controlled study; correlation coefficient; female; human; intractable epilepsy; male; outcome assessment; predictor variable; priority journal; statistical analysis |
顯示於: | 醫學系 |
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