Bayesian analysis of transformation latent variable models with multivariate censored data

Statistical Methods in Medical Research
Xin-Yuan SongJing-Heng Cai

Abstract

Transformation latent variable models are proposed in this study to analyze multivariate censored data. The proposed models generalize conventional linear transformation models to semiparametric transformation models that accommodate latent variables. The characteristics of the latent variables were assessed based on several correlated observed indicators through measurement equations. A Bayesian approach was developed with Bayesian P-splines technique and the Markov chain Monte Carlo algorithm to estimate the unknown parameters and transformation functions. Simulation shows that the performance of the proposed methodology is satisfactory. The proposed method was applied to analyze a cardiovascular disease data set.

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Citations

Mar 9, 2017·Biometrical Journal. Biometrische Zeitschrift·Jingheng CaiLiuquan Sun
Dec 12, 2017·Statistical Methods in Medical Research·Deng PanXinyuan Song
Jan 18, 2018·Statistical Methods in Medical Research·Jingheng Cai, Chenyi Liang
Jun 15, 2019·Statistics in Medicine·Chunjie WangXiaogang Dong
Nov 20, 2016·Statistics in Medicine·Haijin HeLiuquan Sun
Sep 17, 2021·Statistics in Medicine·Haijin HeLiuquan Sun

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