PMID- 35706842
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20220716
IS  - 0266-4763 (Print)
IS  - 1360-0532 (Electronic)
IS  - 0266-4763 (Linking)
VI  - 47
IP  - 12
DP  - 2020
TI  - The multinomial logistic regression model for predicting the discharge status 
      after liver transplantation: estimation and diagnostics analysis.
PG  - 2159-2177
LID - 10.1080/02664763.2019.1706725 [doi]
AB  - 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.
CI  - (c) 2019 Informa UK Limited, trading as Taylor & Francis Group.
FAU - Hashimoto, E M
AU  - Hashimoto EM
AD  - Departamento Academico de Matematica, Universidade Tecnologica Federal do Parana, 
      Londrina, PR, Brazil.
FAU - Ortega, E M M
AU  - Ortega EMM
AUID- ORCID: 0000-0003-3999-7402
AD  - Departamento de Ciencias Exatas, Universidade de S ao Paulo, Piracicaba, SP, 
      Brazil.
FAU - Cordeiro, G M
AU  - Cordeiro GM
AUID- ORCID: 0000-0002-3052-6551
AD  - Departamento de Estatistica, Universidade Federal de Pernambuco, Recife, PE, 
      Brazil.
FAU - Suzuki, A K
AU  - Suzuki AK
AD  - Departamento de Matematica Aplicada e Estatistica, Universidade de S ao Paulo, 
      S ao Carlos, SP, Brazil.
FAU - Kattan, M W
AU  - Kattan MW
AD  - Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH, USA.
LA  - eng
PT  - Journal Article
DEP - 20191224
PL  - England
TA  - J Appl Stat
JT  - Journal of applied statistics
JID - 9883455
PMC - PMC9041638
OTO - NOTNLM
OT  - Categorical data
OT  - diagnostic analysis
OT  - multinomial distribution
OT  - nominal response
OT  - regression model
COIS- No potential conflict of interest was reported by the authors.
EDAT- 2019/12/24 00:00
MHDA- 2019/12/24 00:01
PMCR- 2019/12/24
CRDT- 2022/06/16 02:29
PHST- 2022/06/16 02:29 [entrez]
PHST- 2019/12/24 00:00 [pubmed]
PHST- 2019/12/24 00:01 [medline]
PHST- 2019/12/24 00:00 [pmc-release]
AID - 1706725 [pii]
AID - 10.1080/02664763.2019.1706725 [doi]
PST - epublish
SO  - J Appl Stat. 2019 Dec 24;47(12):2159-2177. doi: 10.1080/02664763.2019.1706725. 
      eCollection 2020.