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Regularization method for predicting an ordinal response using longitudinal high-dimensional genomic data

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  • Hou Jiayi
  • Archer Kellie J.

    (Department of Biostatistics, Virginia Commonwealth University, VA, USA)

Abstract

An ordinal scale is commonly used to measure health status and disease related outcomes in hospital settings as well as in translational medical research. In addition, repeated measurements are common in clinical practice for tracking and monitoring the progression of complex diseases. Classical methodology based on statistical inference, in particular, ordinal modeling has contributed to the analysis of data in which the response categories are ordered and the number of covariates (p) remains smaller than the sample size (n). With the emergence of genomic technologies being increasingly applied for more accurate diagnosis and prognosis, high-dimensional data where the number of covariates (p) is much larger than the number of samples (n), are generated. To meet the emerging needs, we introduce our proposed model which is a two-stage algorithm: Extend the generalized monotone incremental forward stagewise (GMIFS) method to the cumulative logit ordinal model; and combine the GMIFS procedure with the classical mixed-effects model for classifying disease status in disease progression along with time. We demonstrate the efficiency and accuracy of the proposed models in classification using a time-course microarray dataset collected from the Inflammation and the Host Response to Injury study.

Suggested Citation

  • Hou Jiayi & Archer Kellie J., 2015. "Regularization method for predicting an ordinal response using longitudinal high-dimensional genomic data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 14(1), pages 93-111, February.
    • Handle: RePEc:bpj:sagmbi:v:14:y:2015:i:1:p:93-111:n:2
    DOI: 10.1515/sagmb-2014-0004
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    References listed on IDEAS

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    1. Yuan, Ming & Kendziorski, Christina, 2006. "Hidden Markov Models for Microarray Time Course Data in Multiple Biological Conditions," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1323-1332, December.
    2. Howard D. Bondell & Arun Krishna & Sujit K. Ghosh, 2010. "Joint Variable Selection for Fixed and Random Effects in Linear Mixed-Effects Models," Biometrics, The International Biometric Society, vol. 66(4), pages 1069-1077, December.
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