Multiple-response logistic regression modeling with application to an analysis of cirrhosis liver disease data

Yang Jing-Nan, Tian Yu-Zhu (), Wang Yue and Wu Chun-Ho
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Yang Jing-Nan: Northwest Normal University
Tian Yu-Zhu: Northwest Normal University
Wang Yue: The Education University of Hong Kong
Wu Chun-Ho: The Hang Seng University of Hong Kong

Computational Statistics, 2025, vol. 40, issue 5, No 12, 2634 pages

Abstract: Abstract In practical data analysis, individual measurements usually include two or more responses, and some statistical correlations often exist between the responses. Especially in medical data analysis, observations are often binary responses. A class of multi-response logistic regression model based on a joint modeling approach is investigated in this paper, and an application to a group data of primary biliary cirrhosis diseases is considered. Firstly, we propose a new class of multi-response logistic distribution and investigate its statistical properties. Secondly, a multi-response logistic regression model is constructed using a latent variable model and multi-variate logistic error distribution. Furthermore, the parameter estimation method of the model is provided by applying the monte carlo expectation maximization (MCEM) algorithm and the multiple imputation method. Finally, numerical simulations and comparative predictions on a test set are performed to validate the finite sample performance of the proposed model, and the model is applied to a cirrhosis disease dataset for analysis.

Keywords: Multi-response logistic regression; Latent variable model; MCEM algorithm; Multiple imputation; Primary biliary cirrhosis (PBC) (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s00180-024-01575-1

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