The multinomial logistic regression model for predicting the discharge status after liver transplantation: estimation and diagnostics analysis

E M Hashimoto; E M M Ortega; G M Cordeiro; A K Suzuki; M W Kattan

doi:10.1080/02664763.2019.1706725

The multinomial logistic regression model for predicting the discharge status after liver transplantation: estimation and diagnostics analysis

J Appl Stat. 2019 Dec 24;47(12):2159-2177. doi: 10.1080/02664763.2019.1706725. eCollection 2020.

Authors

E M Hashimoto¹, E M M Ortega², G M Cordeiro³, A K Suzuki⁴, M W Kattan⁵

Affiliations

¹ Departamento Acadêmico de Matemática, Universidade Tecnológica Federal do Paraná, Londrina, PR, Brazil.
² Departamento de Ciências Exatas, Universidade de S ao Paulo, Piracicaba, SP, Brazil.
³ Departamento de Estatística, Universidade Federal de Pernambuco, Recife, PE, Brazil.
⁴ Departamento de Matemática Aplicada e Estatística, Universidade de S ao Paulo, S ao Carlos, SP, Brazil.
⁵ Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH, USA.

Abstract

The multinomial logistic regression model (MLRM) can be interpreted as a natural extension of the binomial model with logit link function to situations where the response variable can have three or more possible outcomes. In addition, when the categories of the response variable are nominal, the MLRM can be expressed in terms of two or more logistic models and analyzed in both frequentist and Bayesian approaches. However, few discussions about post modeling in categorical data models are found in the literature, and they mainly use Bayesian inference. The objective of this work is to present classic and Bayesian diagnostic measures for categorical data models. These measures are applied to a dataset (status) of patients undergoing kidney transplantation.

Keywords: Categorical data; diagnostic analysis; multinomial distribution; nominal response; regression model.