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Multiple Comparisons Using Composite Likelihood in Clustered Data

Author

Listed:
  • Azadbakhsh Mahdis

    (Department of Statistics and Mathematics, York University, Toronto, ON M3J 1P3, Canada)

  • Gao Xin

    (Department of Statistics and Mathematics, York University, Toronto, ON M3J 1P3, Canada)

  • Jankowski Hanna

    (Department of Statistics and Mathematics, York University, Toronto, ON M3J 1P3, Canada)

Abstract

We study the problem of multiple hypothesis testing for correlated clustered data. As the existing multiple comparison procedures based on maximum likelihood estimation could be computationally intensive, we propose to construct multiple comparison procedures based on composite likelihood method. The new test statistics account for the correlation structure within the clusters and are computationally convenient to compute. Simulation studies show that the composite likelihood based procedures maintain good control of the familywise type I error rate in the presence of intra-cluster correlation, whereas ignoring the correlation leads to erratic performance.

Suggested Citation

  • Azadbakhsh Mahdis & Gao Xin & Jankowski Hanna, 2016. "Multiple Comparisons Using Composite Likelihood in Clustered Data," The International Journal of Biostatistics, De Gruyter, vol. 12(2), pages 1-12, November.
    • Handle: RePEc:bpj:ijbist:v:12:y:2016:i:2:p:12:n:13
    DOI: 10.1515/ijb-2016-0004
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    References listed on IDEAS

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    1. Cristiano Varin, 2008. "On composite marginal likelihoods," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 92(1), pages 1-28, February.
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