Mixture random effect model based meta-analysis for medical data mining

  • Authors:

  • Yinglong Xia;Shifeng Weng;Changshui Zhang;Shao Li

  • Affiliations:

  • State Key Laboratory of Intelligent Technology and Systems, Department of Automation, Tsinghua University, Beijing, China;State Key Laboratory of Intelligent Technology and Systems, Department of Automation, Tsinghua University, Beijing, China;State Key Laboratory of Intelligent Technology and Systems, Department of Automation, Tsinghua University, Beijing, China;State Key Laboratory of Intelligent Technology and Systems, Department of Automation, Tsinghua University, Beijing, China

  • Venue:
  • MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
  • Year:
  • 2005

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Abstract

As a powerful tool for summarizing the distributed medical information, Meta-analysis has played an important role in medical research in the past decades. In this paper, a more general statistical model for meta-analysis is proposed to integrate heterogeneous medical researches efficiently. The novel model, named mixture random effect model (MREM), is constructed by Gaussian Mixture Model (GMM) and unifies the existing fixed effect model and random effect model. The parameters of the proposed model are estimated by Markov Chain Monte Carlo (MCMC) method. Not only can MREM discover underlying structure and intrinsic heterogeneity of meta datasets, but also can imply reasonable subgroup division. These merits embody the significance of our methods for heterogeneity assessment. Both simulation results and experiments on real medical datasets demonstrate the performance of the proposed model.