Bayesian random-effect model for predicting outcome fraught with heterogeneity: An illustration with episodes of 44 patients with intractable epilepsy

A. M F Yen, H. H. Liou, H. L. Lin, Tony Hsiu Hsi Chen

研究成果: 雜誌貢獻文章同行評審

3 引文 斯高帕斯(Scopus)

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.
原文英語
頁(從 - 到)631-637
頁數7
期刊Methods of Information in Medicine
45
發行號6
出版狀態已發佈 - 2006
對外發佈

ASJC Scopus subject areas

  • 健康資訊學
  • 健康資訊管理
  • 護理(全部)

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