| アブストラクト | We propose a generalized linear low-rank mixed model (GLLRM) for the analysis of both high-dimensional and sparse responses and covariates where the responses may be binary, counts, or continuous. This development is motivated by the problem of identifying vaccine-adverse event associations in post-market drug safety databases, where an adverse event is any untoward medical occurrence or health problem that occurs during or following vaccination. The GLLRM is a generalization of a generalized linear mixed model in that it integrates a factor analysis model to describe the dependence among responses and a low-rank matrix to approximate the high-dimensional regression coefficient matrix. A sampling procedure combining the Gibbs sampler and Metropolis and Gamerman algorithms is employed to obtain posterior estimates of the regression coefficients and other model parameters. Testing of response-covariate pair associations is based on the posterior distribution of the corresponding regression coefficients. Monte Carlo simulation studies are conducted to examine the finite-sample performance of the proposed procedures on binary and count outcomes. We further illustrate the GLLRM via a real data example based on the Vaccine Adverse Event Reporting System. |
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| ジャーナル名 | Statistics in medicine |
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| 投稿日 | 2023/3/29 |
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| 投稿者 | Hauser, Paloma; Tan, Xianming; Chen, Fang; Ibrahim, Joseph G |
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| 組織名 | Department of Biostatistics, University of North Carolina, Chapel Hill, 27599,;North Carolina, USA.;SAS Institute Inc., Cary, 27513, North Carolina, USA. |
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| Pubmed リンク | https://www.ncbi.nlm.nih.gov/pubmed/36974659/ |
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