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On the identification of predictive biomarkers: Detecting treatment-by-gene interaction in high-dimensional data

Author

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  • Werft, W.
  • Benner, A.
  • Kopp-Schneider, A.

Abstract

For personalised medicine the identification of predictive biomarkers is of great interest. These could guide the choice of therapy and could therefore optimise the benefits of patients of such treatments. The technology of gene expression microarrays allows one to scan thousands of potentially predictive biomarkers simultaneously. In clinical trials it has nowadays become common to use microarrays to collect gene expression data of the patients before treatment. The identification of predictive biomarkers can be statistically addressed by inference of gene-wise generalised linear models (GLM) including an interaction term gene expression times treatment. Inference for such GLMs is then often based on likelihood-ratio (LR) or Wald test statistics to test the influence of interaction of gene expression and treatment on the clinical treatment response. For multiple testing scenarios coming along with these gene-wise GLMs the control of the false discovery rate (FDR) would be appropriate; some false positives can be tolerated within a list of potential candidate genes which deserve further investigation. In a simulation study the utility of various FDR controlling multiple testing procedures for the identification of predictive genes is examined. Since the usual experiment on microarray data deals with small numbers of observations due to financial or probe limitations special interest lies on the behaviour of small sample sizes. Results reveal that a permutation of regressor residuals (PRR) test is superior to standard LR and Wald tests in terms of FDR control.

Suggested Citation

  • Werft, W. & Benner, A. & Kopp-Schneider, A., 2012. "On the identification of predictive biomarkers: Detecting treatment-by-gene interaction in high-dimensional data," Computational Statistics & Data Analysis, Elsevier, vol. 56(5), pages 1275-1286.
    • Handle: RePEc:eee:csdana:v:56:y:2012:i:5:p:1275-1286
    DOI: 10.1016/j.csda.2010.11.019
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    References listed on IDEAS

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    1. Efron B. & Tibshirani R. & Storey J.D. & Tusher V., 2001. "Empirical Bayes Analysis of a Microarray Experiment," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1151-1160, December.
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    1. Chen, Bingshu E. & Jiang, Wenyu & Tu, Dongsheng, 2014. "A hierarchical Bayes model for biomarker subset effects in clinical trials," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 324-334.
    2. Soyeon Kim & Veerabhadran Baladandayuthapani & J. Jack Lee, 2017. "Prediction-Oriented Marker Selection (PROMISE): With Application to High-Dimensional Regression," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 9(1), pages 217-245, June.

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