ArticleJournal of applied statistics2020
The multinomial logistic regression model for predicting the discharge status after liver transplantation: estimation and diagnostics analysis.
Article in Journal of applied statistics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed.
- A comparison of modeling approaches for static and dynamic prediction of central-line bloodstream infections using electronic health records (part 1): regression models.Diagnostic and prognostic research · 2025Article
- Leveraging machine learning in precision medicine to unveil organochlorine pesticides as predictive biomarkers for thyroid dysfunction.Scientific reports · 2025Article
- Regression analysis for cardiothoracic surgeons: part 5-predicting risks and outcomes in surgery.Indian journal of thoracic and cardiovascular surgery · 2025Article
- Promoting Adolescents' Heritage Cultural Identity Development: Exploring the Role of Autonomy and Relatedness Satisfaction in School-Based Interventions.Journal of youth and adolescence · 2024Article
- Harmonization of supervised machine learning practices for efficient source attribution of Listeria monocytogenes based on genomic data.BMC genomics · 2023Article
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Authors and funding
5 authors.
Funding
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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.
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