scholarly journals A novel method for testing goodness of fit of a proportional odds model: an application to an AIDS study

2008 ◽  
Vol 36 (2) ◽  
pp. 125 ◽  
Author(s):  
WWM Abeysekera ◽  
Roshini Sooriyarachchi
Keyword(s):  
Goodness Of Fit ◽  
Proportional Odds Model ◽  
Proportional Odds ◽  
Novel Method ◽  
Aids Study

scholarly journals Ordinal logistic regression models: application in quality of life studies

2008 ◽  
Vol 24 (suppl 4) ◽  
pp. s581-s591 ◽  
Author(s):  
Mery Natali Silva Abreu ◽  
Arminda Lucia Siqueira ◽  
Clareci Silva Cardoso ◽  
Waleska Teixeira Caiaffa
Keyword(s):  
Quality Of Life ◽  
Logistic Regression ◽  
Regression Models ◽  
Goodness Of Fit ◽  
Ordinal Logistic Regression ◽  
Proportional Odds Model ◽  
Outcome Variable ◽  
Proportional Odds ◽  
Logistic Regression Models

Quality of life has been increasingly emphasized in public health research in recent years. Typically, the results of quality of life are measured by means of ordinal scales. In these situations, specific statistical methods are necessary because procedures such as either dichotomization or misinformation on the distribution of the outcome variable may complicate the inferential process. Ordinal logistic regression models are appropriate in many of these situations. This article presents a review of the proportional odds model, partial proportional odds model, continuation ratio model, and stereotype model. The fit, statistical inference, and comparisons between models are illustrated with data from a study on quality of life in 273 patients with schizophrenia. All tested models showed good fit, but the proportional odds or partial proportional odds models proved to be the best choice due to the nature of the data and ease of interpretation of the results. Ordinal logistic models perform differently depending on categorization of outcome, adequacy in relation to assumptions, goodness-of-fit, and parsimony.

scholarly journals A COMPARISON OF POLYTOMOUS MODEL WITH PROPORTIONAL ODDS AND NON-PROPORTIONAL ODDS MODEL ON BIRTH SIZE CASE IN INDONESIA

2021 ◽  
Vol 14 (1) ◽  
pp. 79-88
Author(s):  
Yenni Kurniawati ◽  
Anang Kurnia ◽  
Kusman Sadik
Keyword(s):  
Risk Factors ◽  
Significant Influence ◽  
Alternative Model ◽  
Goodness Of Fit ◽  
Proportional Odds Model ◽  
Birth Size ◽  
Proportional Odds ◽  
Proportional Odds Assumption ◽  
Deviance Test ◽  
And Gender

The proportional odds model (POM) and the non-proportional odds model (NPOM) are very useful in ordinal modeling. However, the proportional odds assumption is often violated in practice. In this paper, the non-proportional odds model is chosen as an alternative model when the proportional odds assumption is not violated. This paper aims to compare Proportional Odds Model (POM) and Non-Proportional Odds Model (NPOM) in cases of birth size in Indonesia based on the 2017 Indonesian Demographic and Health Survey (IDHS) data. The results showed that in the POM there was a violation of the proportional odds assumption, so the alternative NPOM model was used. NPOM had better use than POM. The goodness of fit shows that the deviance test failed to reject H0, and the value of Mac Fadden R2 is higher than POM. The risk factors that have a significant influence on all categories of birth size are the residence and gender of the child.

scholarly journals Examining partial proportional odds model in analyzing severity of high-speed railway accident

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jing Wang ◽  
Yinghan Wang ◽  
Yichuan Peng ◽  
Jian John Lu
Keyword(s):  
High Speed ◽  
Proportional Odds Model ◽  
Impact Factors ◽  
Proportional Odds ◽  
High Speed Railway ◽  
Severity Level ◽  
Content Type ◽  
Factors Affecting ◽  
Partial Proportional Odds Model ◽  
The Impact

Purpose The operation safety of the high-speed railway has been widely concerned. Due to the joint influence of the environment, equipment, personnel and other factors, accidents are inevitable in the operation process. However, few studies focused on identifying contributing factors affecting the severity of high-speed railway accidents because of the difficulty in obtaining field data. This study aims to investigate the impact factors affecting the severity of the general high-speed railway. Design/methodology/approach A total of 14 potential factors were examined from 475 data. The severity level is categorized into four levels by delay time and the number of subsequent trains that are affected by the accident. The partial proportional odds model was constructed to relax the constraint of the parallel line assumption. Findings The results show that 10 factors are found to significantly affect accident severity. Moreover, the factors including automation train protection (ATP) system fault, platform screen door and train door fault, traction converter fault and railway clearance intrusion by objects have an effect on reducing the severity level. On the contrary, the accidents caused by objects hanging on the catenary, pantograph fault, passenger misconducting or sudden illness, personnel intrusion of railway clearance, driving on heavy rain or snow and train collision against objects tend to be more severe. Originality/value The research results are very useful for mitigating the consequences of high-speed rail accidents.

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