Generalized linear mixed models with Gaussian mixture random effects: Inference and application

Lanfeng Pan, Yehua Li, Kevin He, Yanming Li and Yi Li

Journal of Multivariate Analysis, 2020, vol. 175, issue C

Abstract: We propose a new class of generalized linear mixed models with Gaussian mixture random effects for clustered data. To overcome the weak identifiability issues, we fit the model using a penalized Expectation Maximization (EM) algorithm, and develop sequential locally restricted likelihood ratio tests to determine the number of components in the Gaussian mixture. Our work is motivated by an application to nationwide kidney transplant center evaluation in the United States, where the patient-level post-surgery outcomes are repeated measures of the care quality of the transplant centers. By taking into account patient-level risk factors and modeling the center effects by a finite Gaussian mixture model, the proposed model provides a convenient framework to study the heterogeneity among the transplant centers and controls the false discovery rate when screening for transplant centers with non-standard performance.

Keywords: Clustering; False discovery rate; Latent variables; Locally restricted likelihood ratio test; Penalized EM algorithm; Repeated measure (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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DOI: 10.1016/j.jmva.2019.104555

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