Adjusting for Confounding by Neighborhood Using a Proportional Odds Model and Complex Survey Data

In social epidemiology, an individual\u27s neighborhood is considered to be an important determinant of health behaviors, mediators, and outcomes. Consequently, when investigating health disparities, researchers may wish to adjust for confounding by unmeasured neighborhood factors, such as local availability of health facilities or cultural predispositions. With a simple random sample and a binary outcome, a conditional logistic regression analysis that treats individuals within a neighborhood as a matched set is a natural method to use. The authors present a generalization of this method for ordinal outcomes and complex sampling designs. The method is based on a proportional odds model and is very simple to program using standard software such as SAS PROC SURVEYLOGISTIC (SAS Institute Inc., Cary, North Carolina). The authors applied the method to analyze racial/ethnic differences in dental preventative care, using 2008 Florida Behavioral Risk Factor Surveillance System survey data. The ordinal outcome represented time since last dental cleaning, and the authors adjusted for individual-level confounding by gender, age, education, and health insurance coverage. The authors compared results with and without additional adjustment for confounding by neighborhood, operationalized as zip code. The authors found that adjustment for confounding by neighborhood greatly affected the results in this example

VGLM proportional odds model to infer hosts’ Airbnb performance

We investigated aspects of host activities that influence and enhance host performance in an effort to achieve best results in terms of the occupancy rate and the overall rating. The occupancy rate measures the percentage of reserved days with respect to available days. The overall rating identifies the satisfaction level of guests that booked an Airbnb accommodation. We used the proportional odds model to estimate the impact of the managerial variables and the characteristics of the accommodation on host performance. Five different levels of the occupancy and the overall rating were investigated to understand which features impact them and support the effort to move from the lowest to the highest level. The analysis was carried out for Italy’s most visited cities: Rome, Milan, Venice, and Florence. We focused on the year 2016. Moreover, we investigated different impact levels in terms of the overall rating during the COVID-19 pandemic to evaluate possible differences. Our findings show the relevance of some variables, such as the number of reviews, services, and typology of the rented accommodation. Moreover, the results show differences among cities and in time for the relevant impact of the COVID-19 pandemic

The Proportional Odds Model for Assessing Rater Agreement with Multiple Modalities

In this paper, we develop a model for evaluating an ordinal rating systems where we assume that the true underlying disease state is continuous in nature. Our approach in motivated by a dataset with 35 microscopic slides with 35 representative duct lesions of the pancreas. Each of the slides was evaluated by eight raters using two novel rating systems (PanIN illustrations and PanIN nomenclature),where each rater used each systems to rate the slide with slide identity masked between evaluations. We find that the two methods perform equally well but that differentiation of higher grade lesions is more consistent across raters than differentiation across raters for lower grade lesions. A proportional odds model is assumed, which allows us to estimate rater-specific thresholds for comparing agreement. In this situation where we have two methods of rating, we can determine whether the two methods have the same thresholds and whether or not raters perform equivalently across methods. Unlike some other model-based approaches for measuring agreement, we focus on the interpretation of the model parameters and their scientific relevance. We compare posterior estimates of rater-specific parameters across raters to see if they are implementing the intended rating system in the same manner. Estimated standard deviation distributions are used to make inferences as to whether raters are consistent and whether there are differences in rating behaviors in the two rating systems under comparison

Risk Factors Associated with Stunting and Wasting Levels Among Under Five Children in Ethiopia

Introduction: Childhood stunting is one of the most significant impediments to human development. Stunting is a major health problem in children under-five years in many low and middle income countries around the world. Wasting is sometimes referred to as acute malnutrition because it is believed that episodes of wasting have a short duration, in contrast to stunting, which is regarded as chronic malnutrition.Method: The data for the study were taken from Ethiopian Demographic Health Survey (EDHS) of year 2011. For stunting levels parallel line assumption of proportional odds model is violated. Thus, Partial proportional odds model was preferred over proportional odds model, generalized ordered logit model and multinomial logistic regression based on Akaike’s Information Criterion evidence. Proportional odds model is used to analyze wasting levels since the parallel assumption of proportional odds model is not violated. Result: This study revealed that the relative frequency distributions of the stunting and wasting status of child. 16.5% are severely stunted, 20.6% are moderately stunted and 62.9% are not stunted and also shows that 1.4% of children are severely wasted, 9% are moderately wasted and 89.6% are not wasted.  The result indicates that age of child in month, region, place of residence, wealth index, mothers BMI, birth order of child, incidence of diarrhea for two weeks preceding the survey, incidence of fever for two weeks before survey, mothers and husband/partner educational levels are significantly associated with stunting levels. The result also shows that age of child, wealth index, mothers nutritional status, sex of child, incidence of diarrhea and fever for two weeks before survey, type of toilet, husbands/partner and employment status of mothers are significantly associated with wasting levels. Conclusions: PPOM fitted the data adequately in predicting severity status of stunting because of POM assumption is violated but POM is appropriate for wasting status. Children younger than 11 months had low risk of stunting and wasting status than other age groups. This could be because of breastfeeding in the early stages of child growth. Children in rural areas are more likely to be stunted than children in urban areas. Keywords: Stunting and wasting, Proportional odds model, partial proportional odds model. DOI: 10.7176/JHMN/63-01 Publication date:June 30th 201

Application of Multinomial and Ordinal Regressions to the Data of Japanese Female Labor Market

This paper describes the application of ordered and unordered multinomial approaches to Japanese Female Labor Market data with the goal of examining how inter-organizational networks linking schools to large corporations supersede labor market processes in the Japanese female labor market. Two sets of response categories were used for a proportional odds model, a non-proportional odds model, and a multinomial logit model. The results from the six combinations of these models were compared in terms of their goodness of model fit. The results showed that the proportional odds assumption was weakly supported, and the Wald test indicates that the violation of proportional odds assumption seems to be limited to a single variable. My study implies that partially proportional odds model would yield a better fit to my female labor market data

Methodological Quality and Reporting of Regression Models for Ordinal Responses in Sports Field: A Scoping Review

Treballs Finals de Grau en Estadística UB-UPC, Facultat d'Economia i Empresa (UB) i Facultat de Matemàtiques i Estadística (UPC), Curs: 2022-2023, Tutor: Daniel Fernández Martínez i Martí Casals ToqueroThe integration of statistical methods in sports science has become essential for decision-making in performance analysis, injury prevention, and athlete outcomes. This work presents a scoping review following PRISMA guidelines to explore the application of ordinal regression models in the sports field. A comprehensive search of articles published, until March 4, 2023, identified 34 included studies. This search included widely recognized databases such as Web of Science, PubMed, and specialized journals of sports statistics, such as Journal of Quantitative Analysis in Sports, and Journal of Sports Analytics. The analysis reveals that 26.5% of these articles were published in statistics and sports statistics journals. However, a significant majority (82.4%) of the studies did not provide data and code repositories. Notably, R emerged as the primary software used for analysis in 38.8% of the studies. Football had the highest representation (28.6%), followed by basketball (17.1%). The most commonly reported ordinal model was the proportional odds model (32.3%), followed by the mixed effects proportional odds model (11.8%), while a relevant proportion (29.4%) did not report the model used. Furthermore, 23.5% of articles proposed novel models. Validation test for proportional odds model were not conducted in 53.3% of cases. This review underscores the importance of improved reporting practices, inclusivity in sport representation, and statistical education in advancing sports analytics. In addition to the scoping review, a real case example to demonstrate the application of the proportional odds model and the mixed effects proportional odds model in the sports field is provided. This case example aims to showcase the practical implementation of these statistical methods and their potential impact on decision-making in sports analytics

Ordinal Regression Analysis: Fitting the Proportional Odds Model Using Stata, SAS and SPSS

Researchers have a variety of options when choosing statistical software packages that can perform ordinal logistic regression analyses. However, statistical software, such as Stata, SAS, and SPSS, may use different techniques to estimate the parameters. The purpose of this article is to (1) illustrate the use of Stata, SAS and SPSS to fit proportional odds models using educational data; and (2) compare the features and results for fitting the proportional odds model using Stata OLOGIT, SAS PROC LOGISTIC (ascending and descending), and SPSS PLUM. The assumption of the proportional odds was tested, and the results of the fitted models were interpreted

Ordinal regression modelling between proportional odds and non-proportional odds

The proportional odds model has become the most widely used model in ordinal regression. Despite favourable properties in applications it is often an inappropriate simplification yielding bad data fit. The more flexible non-proportional odds model or partial proportional odds model have the disadvantage that common estimation procedures as Fisher scoring often fail to converge. Then neither estimates nor test statistics for the validity of partial proportional odds models are available. In the present paper estimates are proposed which are based on penalization of parameters across response categories. For appropriate smoothing penalized estimates exist almost always and are used to derive test statistics for the assumption of partial proportional odds. In addition, models are considered where the variation of parameters across response categories is constrained. Instead of using prespecified scalars (Peterson&Harrell 1990) penalized estimates are used in the identification of these constrained models. The methods are illustrated by various applications. The application to the retinopathy status in chronic diabetes shows how the proposed test statistics may be used in the diagnosis of partial proportional odds models in order to prevent artefacts

Development of a Partial Proportional Odds Model for Pedestrian Injury Severity at Intersections

Pedestrian injury in crashes at intersections often results from complex interaction among various factors. The factor identification is a critical task for understanding the causes and improving the pedestrian safety. A total of 2,614 crash records at signalized and non-signalized intersections were applied. A Partial Proportional Odds (PPO) model was developed to examine the factors influencing Pedestrian Injury Severity (PIS) because it can accommodate the ordered response nature of injury severity. An elasticity analysis was conducted to quantify the marginal effects of contributing factors on the likelihood of PIS. For signalized intersections, seven explanatory variables significantly affect the likelihood of PIS, in which five explanatory variables violate the Proportional Odds Assumption (POA). Local driver, truck, holiday, clear weather, and hit-and-run lead to higher likelihood of severer PIS. For non-signalized intersections, six explanatory variables were found significant to the PIS, in which three explanatory variables violate the POA. Young and adult drivers, senior pedestrian, bus/van, divided road, holiday, and darkness tend to increase the likelihood of severer PIS. The vehicles of large size and heavy weight (e.g. truck, bus/van) are significant factors to the PIS at both signalized and non-signalized intersections. The proposed PPO model has demonstrated its effectiveness in identifying the effects of contributing factors on the PIS.</p