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

• Published in: Biometrics Vol. 79; no. 2; pp. 1073 - 1088
• Main Authors: Zhang, Qihuang, Yi, Grace Y.
• Format: Journal Article
• Language: English
• Published: United States Blackwell Publishing Ltd 01.06.2023
• Subjects: Animals; Biomedical research; Complexity; equations; Error analysis; Gaussian graphical model; gene regulatory network; Gene Regulatory Networks; generalized estimating equation; genes; Genome-Wide Association Study; Genomes; measurement error; Mice; misclassification; Multivariate analysis; Normal Distribution; measurement error; Gaussian graphical model; generalized estimating equation; gene regulatory network; misclassification
• ISSN: 0006-341X; 1541-0420; 1541-0420
• DOI: 10.1111/biom.13623

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.