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Identifying gene-environment interactions for prognosis using a robust approach

Author

Listed:
  • Chai, Hao
  • Zhang, Qingzhao
  • Jiang, Yu
  • Wang, Guohua
  • Zhang, Sanguo
  • Ahmed, Syed Ejaz
  • Ma, Shuangge

Abstract

For many complex diseases, prognosis is of essential importance. It has been shown that, beyond the main effects of genetic (G) and environmental (E) risk factors, gene-environment (G × E) interactions also play a critical role. In practical data analysis, part of the prognosis outcome data can have a distribution different from that of the rest of the data because of contamination or a mixture of subtypes. Literature has shown that data contamination as well as a mixture of distributions, if not properly accounted for, can lead to severely biased model estimation. In this study, we describe prognosis using an accelerated failure time (AFT) model. An exponential squared loss is proposed to accommodate data contamination or a mixture of distributions. A penalization approach is adopted for regularized estimation and marker selection. The proposed method is realized using an effective coordinate descent (CD) and minorization maximization (MM) algorithm. The estimation and identification consistency properties are rigorously established. Simulation shows that without contamination or mixture, the proposed method has performance comparable to or better than the nonrobust alternative. However, with contamination or mixture, it outperforms the nonrobust alternative and, under certain scenarios, is superior to the robust method based on quantile regression. The proposed method is applied to the analysis of TCGA (The Cancer Genome Atlas) lung cancer data. It identifies interactions different from those using the alternatives. The identified markers have important implications and satisfactory stability.

Suggested Citation

  • Chai, Hao & Zhang, Qingzhao & Jiang, Yu & Wang, Guohua & Zhang, Sanguo & Ahmed, Syed Ejaz & Ma, Shuangge, 2017. "Identifying gene-environment interactions for prognosis using a robust approach," Econometrics and Statistics, Elsevier, vol. 4(C), pages 105-120.
    • Handle: RePEc:eee:ecosta:v:4:y:2017:i:c:p:105-120
    DOI: 10.1016/j.ecosta.2016.10.004
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    References listed on IDEAS

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    1. Stute, W., 1993. "Consistent Estimation Under Random Censorship When Covariables Are Present," Journal of Multivariate Analysis, Elsevier, vol. 45(1), pages 89-103, April.
    2. Xueqin Wang & Yunlu Jiang & Mian Huang & Heping Zhang, 2013. "Robust Variable Selection With Exponential Squared Loss," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(502), pages 632-643, June.
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    Cited by:

    1. Qin, Yichen & Wang, Linna & Li, Yang & Li, Rong, 2023. "Visualization and assessment of model selection uncertainty," Computational Statistics & Data Analysis, Elsevier, vol. 178(C).
    2. Yu, Ke & Luo, Shan, 2024. "Rank-based sequential feature selection for high-dimensional accelerated failure time models with main and interaction effects," Computational Statistics & Data Analysis, Elsevier, vol. 197(C).

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