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Generalized network structured models with mixed responses subject to measurement error and misclassification

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  • Qihuang Zhang
  • Grace Y. Yi

Abstract

Research of complex associations between a gene network and multiple responses has attracted increasing attention. A great challenge in analyzing genetic data is posited by the presence of the genetic network that is typically unknown. Moreover, mismeasurement of responses introduces additional complexity to distort usual inferential procedures. In this paper, we consider the problem with mixed binary and continuous responses that are subject to mismeasurement and associated with complex structured covariates. We first start with the case where data are precisely measured. We propose a generalized network structured model and develop a two‐step inferential procedure. In the first step, we employ a Gaussian graphical model to facilitate the covariates network structure, and in the second step, we incorporate the estimated graphical structure of covariates and develop an estimating equation method. Furthermore, we extend the development to accommodating mismeasured responses. We consider two cases where the information on mismeasurement is either known or estimated from a validation sample. Theoretical results are established and numerical studies are conducted to evaluate the finite sample performance of the proposed methods. We apply the proposed method to analyze the outbred Carworth Farms White mice data arising from a genome‐wide association study.

Suggested Citation

  • Qihuang Zhang & Grace Y. Yi, 2023. "Generalized network structured models with mixed responses subject to measurement error and misclassification," Biometrics, The International Biometric Society, vol. 79(2), pages 1073-1088, June.
  • Handle: RePEc:bla:biomet:v:79:y:2023:i:2:p:1073-1088
    DOI: 10.1111/biom.13623
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    References listed on IDEAS

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    1. Guan Yu & Yufeng Liu, 2016. "Sparse Regression Incorporating Graphical Structure Among Predictors," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 707-720, April.
    2. Bing Li & Hyonho Chun & Hongyu Zhao, 2012. "Sparse Estimation of Conditional Graphical Models With Application to Gene Networks," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(497), pages 152-167, March.
    3. Zhijian Chen & Grace Y. Yi & Changbao Wu, 2011. "Marginal methods for correlated binary data with misclassified responses," Biometrika, Biometrika Trust, vol. 98(3), pages 647-662.
    4. Li‐Pang Chen & Grace Y. Yi, 2021. "Analysis of noisy survival data with graphical proportional hazards measurement error models," Biometrics, The International Biometric Society, vol. 77(3), pages 956-969, September.
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