Log in

Relevant bibliographies by topics / Proportional odds

Contents

Journal articles
Dissertations / Theses
Books
Book chapters
Conference papers
Reports

Academic literature on the topic 'Proportional odds'

Author: Grafiati

Published: 4 June 2021

Last updated: 3 February 2022

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Proportional odds.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Proportional odds"

1

Kurniawati, Yenni, Anang Kurnia, and Kusman Sadik. "A COMPARISON OF POLYTOMOUS MODEL WITH PROPORTIONAL ODDS AND NON-PROPORTIONAL ODDS MODEL ON BIRTH SIZE CASE IN INDONESIA." MEDIA STATISTIKA 14, no. 1 (April 16, 2021): 79–88. http://dx.doi.org/10.14710/medstat.14.1.79-88.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

2

Sankaran, P. G., and K. Jayakumar. "On proportional odds models." Statistical Papers 49, no. 4 (January 13, 2007): 779–89. http://dx.doi.org/10.1007/s00362-006-0042-3.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Zahid, Faisal Maqbool, Shahla Ramzan, and Christian Heumann. "Regularized proportional odds models." Journal of Statistical Computation and Simulation 85, no. 2 (July 15, 2013): 251–68. http://dx.doi.org/10.1080/00949655.2013.814133.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Hanson, Timothy, and Mingan Yang. "Bayesian Semiparametric Proportional Odds Models." Biometrics 63, no. 1 (November 13, 2006): 88–95. http://dx.doi.org/10.1111/j.1541-0420.2006.00671.x.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Economou, P., and C. Caroni. "Parametric Proportional Odds Frailty Models." Communications in Statistics - Simulation and Computation 36, no. 6 (November 5, 2007): 1295–307. http://dx.doi.org/10.1080/03610910701569143.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Gao, Xiaoming, Todd A. Schwartz, John S. Preisser, and Jamie Perin. "GEEORD: A SAS macro for analyzing ordinal response variables with repeated measures through proportional odds, partial proportional odds, or non-proportional odds models." Computer Methods and Programs in Biomedicine 150 (October 2017): 23–30. http://dx.doi.org/10.1016/j.cmpb.2017.07.008.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Chen, Shande, and Amita K. Manatunga. "A note on proportional hazards and proportional odds models." Statistics & Probability Letters 77, no. 10 (June 2007): 981–88. http://dx.doi.org/10.1016/j.spl.2007.01.006.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

O'Connell, Ann A., and Xing Liu. "Model Diagnostics for Proportional and Partial Proportional Odds Models." Journal of Modern Applied Statistical Methods 10, no. 1 (May 1, 2011): 139–75. http://dx.doi.org/10.22237/jmasm/1304223240.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Lu, Wenbin, and Hao H. Zhang. "Variable selection for proportional odds model." Statistics in Medicine 26, no. 20 (2007): 3771–81. http://dx.doi.org/10.1002/sim.2833.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Kumar M., Dileep, Sankaran P.G., and Unnikrishnan Nair N. "Proportional odds model – a quantile approach." Journal of Applied Statistics 46, no. 11 (January 29, 2019): 1937–55. http://dx.doi.org/10.1080/02664763.2019.1572724.

Full text

APA, Harvard, Vancouver, ISO, and other styles

More sources

Dissertations / Theses on the topic "Proportional odds"

1

梁翠蓮 and Tsui-lin Leung. "Proportional odds model for survival data." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1999. http://hub.hku.hk/bib/B42575011.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Leung, Tsui-lin. "Proportional odds model for survival data." Click to view the E-thesis via HKUTO, 1999. http://sunzi.lib.hku.hk/hkuto/record/B42575011.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Savaluny, Elly. "Analysis of ordered categorical data : partial proportional odds and stratified models." Thesis, University of Reading, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.326978.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Zhang, Yiran. "Bayesian Variable Selection for High-Dimensional Data with an Ordinal Response." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1565283865507018.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Muttarak, Raya, and Wiraporn Pothisiri. "The Role of Education on Disaster Preparedness: Case Study of 2012 Indian Ocean Earthquakes on Thailand's Andaman Coast." The Resilience Alliance, 2013. http://dx.doi.org/10.5751/ES-06101-180451.

Full text

Abstract:

In this paper we investigate how well residents of the Andaman coast in Phang Nga province, Thailand, are prepared for earthquakes and tsunami. It is hypothesized that formal education can promote disaster preparedness because education enhances individual cognitive and learning skills, as well as access to information. A survey was conducted of 557 households in the areas that received tsunami warnings following the Indian Ocean earthquakes on 11 April 2012. Interviews were carried out during the period of numerous aftershocks, which put residents in the region on high alert. The respondents were asked what emergency preparedness measures they had taken following the 11 April earthquakes. Using the partial proportional odds model, the paper investigates determinants of personal disaster preparedness measured as the number of preparedness actions taken. Controlling for village effects, we find that formal education, measured at the individual, household, and community levels, has a positive relationship with taking preparedness measures. For the survey group without past disaster experience, the education level of household members is positively related to disaster preparedness. The findings also show that disaster-related training is most effective for individuals with high educational attainment. Furthermore, living in a community with a higher proportion of women who have at least a secondary education increases the likelihood of disaster preparedness. In conclusion, we found that formal education can increase disaster preparedness and reduce vulnerability to natural hazards.

APA, Harvard, Vancouver, ISO, and other styles

6

Luo, Junxiang. "Goodness-of-fit tests for proportional odds model with GEE for ordinal categorical responses & estimating sampling frequency in pollen exposure assessment over time." Cincinnati, Ohio : University of Cincinnati, 2006. http://www.ohiolink.edu/etd/view.cgi?acc%5Fnum=ucin1150094586.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Salama, Dina. "Predicting Disease Course in Inflammatory Bowel Disease using Health Administrative Data." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/41978.

Full text

Abstract:

Background: Investigators are often interested in using population-level health administrative data in inflammatory bowel disease (IBD) patients to study disease outcomes, risk factors and treatment effects to enhance knowledge, shape clinical practice and influence health care policy. A major limitation of using health administrative data for these purposes is the lack of detailed clinical data to adjust for the confounding effects of differential disease severity on observed associations. Methods to account for disease severity using administrative variables would offer a major advance to population-level studies in IBD patients. Thus, in this study we aimed to use a cohort of IBD patients from The Ottawa Hospital (TOH) to validate a model that was originally developed in Manitoba for estimating clinical disease course in IBD patients through healthcare utilization measures. Objectives: The objectives of this thesis are: 1) To identify and characterize a reference cohort of IBD patients in the ambulatory clinics of four gastroenterologists from TOH on clinical disease course in the preceding year (reference cohort), based on a Manitoba definition of clinical disease course; 2) To fit a partial proportional odds (PPO) model for predicting IBD course, derived using Manitoba health administrative data, to the reference cohort of IBD patients using Ontario health administrative data; 3) To derive new PPO models of IBD disease course for the reference cohort using Ontario administrative variables and compare model performance; and 4) To apply the models to the Ontario Crohn’s and Colitis cohort (OCCC) to estimate IBD course in Ontario, and compare the distribution to that of the Manitoba IBD population.Methods: We first identified a reference cohort of IBD patients in Ontario from the outpatient clinics at TOH during fiscal year 2015. Through chart review, we classified these patients into one of four clinical disease categories (remission, mild, moderate, or severe) using the Manitoba definition. We linked these patients to Ontario health administrative datasets. Given slight differences in data structure and coding between Manitoba and Ontario, we were unable to directly test the Manitoba model and instead fit a PPO model to the Ontario cohort using analogous administrative variables to those used in the final Manitoba model (“adapted model”). We subsequently derived new PPO models using unique Ontario administrative variables under three strategies: 1) Stepwise variable selection (“stepwise model”); 2) Forced fitting of all variables (“all-variables model”); and 3) Using a two-step modelling algorithm that considered IBD-related hospitalizations separate from other administrative variables (“two-step model”). We then compared model performance from the four strategies. Finally, we applied the models to the Ontario IBD population from 2004 to 2016 and compared model estimates to those from Manitoba. Results: We identified 963 patients with IBD from TOH outpatient clinics, of which 52.3% (n=504) were males, 64.6% (n=622) had Crohn's Disease, and 89.2% (n=859) resided in an urban setting. Based on the Manitoba definition, 64.9% of patients within our reference cohort were classified as remission, while 11.4%, 14.1%, and 9.6% were classified as mild, moderate, and severe disease course, respectively. The adapted model (c-statistic 0.77, goodness-fit p-value 0.28) performed comparably to the other models: the stepwise model (c-statistic 0.77, goodness-fit p-value 0.50), the all-variables model (c-statistic 0.77, goodness-fit p-value 0.53), and the two-step model (c-statistic 0.78, goodness-fit p-value 0.75). The adapted model also resulted in overall similar estimates with regards to the disease course distribution among the Ontario IBD population. However, on closer inspection, our two-step model, in which individuals who had been hospitalized for an IBD-related indication within the past year were assumed to have severe disease, performed better with respect to accurately classifying individuals with moderate or severe disease, without sacrificing discriminative ability. Based on the two-step model, from 2004 to 2016, 89.2-91.2% of the Ontario IBD population was in remission, 0% had mild disease, 2.4-3.2% had moderate disease, and 5.9-8.4% had severe disease. Distribution of disease course among IBD patients in Ontario differed considerably than that in Manitoba. Conclusion: In the absence of clinical information within health administrative data, we present and compare four different models that can be used to partially account for the confounding effect of disease course among IBD patients in future population-based studies using Ontario health administrative data. Given that our models did not perform as originally expected, especially with regards to accurately identifying individuals with more active disease states, we advise researchers to use these models at their own discretion.

APA, Harvard, Vancouver, ISO, and other styles

8

Lara, Evandro de Avila e. "Regressão logística politômica ordinal: Avaliação do potencial de Clonostachys rosea no biocontrole de Botrytis cinerea." Universidade Federal de Viçosa, 2012. http://locus.ufv.br/handle/123456789/4060.

Full text

Abstract:

Made available in DSpace on 2015-03-26T13:32:17Z (GMT). No. of bitstreams: 1 texto completo.pdf: 764829 bytes, checksum: 8dbd03463c4800428f75900ca1340eb0 (MD5) Previous issue date: 2012-07-23
The use of logistic regression modeling as a tool for modeling statistical probability of an event as a function of one or more independents variables, has grown among researchers in several areas, including Phytopathology. At about the dichotomous logistic regression in which the dependent variable is the type binary or dummy, is the extensive number of studies in the literature that discuss the modeling assumptions and the interpretation of the analyzes, as well as alternatives for implementation in statistical packages. However, when the variable response requires the use three or more categories, the number of publications is scarce. This is not only due to the scarcity of relevant publications on the subject, but also the inherent difficulty of coverage on the subject. In this paper we address the applicability of the model polytomous ordinal logistic regression, as well as differences between the proportional odds models, nonproportional and partial proportional odds. For this, we analyzed data from an experiment in which we evaluated the potential antagonistic fungus Clonostachys rosea in biocontrol of the disease called "gray mold", caused by Botrytis cinerea in strawberry and tomato. The partial proportional odds models and nonproportional were adjusted and compared, since the proportionality test score accused rejection of the proportional odds assumption. The estimates of the model coefficients as well as the odds ratios were interpreted in practical terms for Phytopathology. The polytomous ordinal logistic regression is introduced as an important statistical tool for predicting values, showing the potential of C. rosea in becoming a commercial product to be developed and used in the biological control of the disease, because the application of C. rosea was as or more effective than the use of fungicides in the control of gray mold.
O uso da regressão logística como uma ferramenta estatística para modelar a probabilidade de um evento em função de uma ou mais variáveis explicativas, tem crescido entre pesquisadores em várias áreas, inclusive na Fitopatologia. À respeito da regressão logística dicotômica, na qual a variável resposta é do tipo binária ou dummy, é extenso o número de trabalhos na literatura que abordam a modelagem, as pressuposições e a interpretação das análises, bem como alternativas de implementação em pacotes estatísticos. No entanto, quando a variável resposta requer que se utilize três ou mais categorias, o número de publicações é escasso. Isso devido não somente à escassez de publicações relevantes sobre o assunto, mas também à inerente dificuldade de abrangência sobre o tema. No presente trabalho aborda-se a aplicabilidade do modelo de regressão logística politômica ordinal, bem como as diferenças entre os modelos de chances proporcionais, chances proporcionais parciais e chances não proporcionais. Para isso, foram analisados dados de um experimento em que se avaliou o potencial do fungo antagonista Clonostachys rosea no biocontrole da doença denominada mofo cinzento , causada por Botrytis cinerea em morangueiro e tomateiro. Os modelos de chances proporcionais parciais e não proporcionais foram ajustados e comparados, uma vez que o teste score de proporcionalidade acusou rejeição da pressuposição de chances proporcionais. As estimativas dos coeficientes dos modelos bem como das razões de chances foram interpretadas em termos práticos para a Fitopatologia. A regressão logística politômica ordinal se apresentou como uma importante ferramenta estatística para predição de valores, mostrando o potencial do C. rosea em se tornar um produto comercial a ser desenvolvido e usado no controle biológico da doença, pois a aplicação de C. rosea foi tão ou mais eficiente do que a utilização de fungicidas no controle do mofo cinzento.

APA, Harvard, Vancouver, ISO, and other styles

9

Capuano, Ana W. "Constrained ordinal models with application in occupational and environmental health." Diss., University of Iowa, 2012. https://ir.uiowa.edu/etd/2450.

Full text

Abstract:

Occupational and environmental epidemiological studies often involve ordinal data, including antibody titer data, indicators of health perceptions, and certain psychometrics. Ideally, such data should be analyzed using approaches that exploit the ordinal nature of the scale, while making a minimum of assumptions. In this work, we first review and illustrate the analytical technique of ordinal logistic regression called the "proportional odds model". This model, which is based on a constrained ordinal model, is considered the most popular ordinal model. We use hypothetical data to illustrate a situation where the proportional odds model holds exactly, and we demonstrate through derivations and simulations how using this model has better statistical power than simple logistic regression. The section concludes with an example illustrating the use of the model in avian and swine influenza research. In the middle section of this work, we show how the proportional model assumption can be relaxed to a less restrictive model called the "trend odds model". We demonstrate how this model is related to latent logistic, normal, and exponential distributions. In particular, scale changes in these potential latent distributions are found to be consistent with the trend odds assumption, with the logistic and exponential distributions having odds that increase in a linear or nearly linear fashion. Actual data of antibody titer against avian and swine influenza among occupationally- exposed participants and non-exposed controls illustrate the fit and interpretation of the proportional odds model and the trend odds model. Finally, we show how to perform a multivariable analysis in which some of the variables meet the proportional model assumption and some meet the trend odds assumption. Likert-scaled data pertaining to violence among middle school students illustrate the fit and interpretation of the multivariable proportional-trend odds model. In conclusion, the proportional odds model provides superior power compared to models that employ arbitrary dichotomization of ordinal data. In addition, the added complexity of the trend odds model provides improved power over the proportional odds model when there are moderate to severe departures from proportionality. The increase in power is of great public health relevance in a time of increasingly scarce resources for occupational and environmental health research. The trend odds model indicates and tests the presence of a trend in odds, providing a new dimension to risk factors and disease etiology analyses. In addition to applications demonstrated in this work, other research areas in occupational and environmental health can benefit from the use of these methods. For example, worker fatigue is often self-reported using ordinal scales, and traumatic brain injury recovery is measured using recovery scores such as the Glasgow Outcome Scale (GOS).

APA, Harvard, Vancouver, ISO, and other styles

10

Cotellesso, Paul. "Statistical and Fuzzy Set Modeling for the Risk Analysis for Critical Infrastructure Protection." The Ohio State University, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=osu1250427229.

Full text

APA, Harvard, Vancouver, ISO, and other styles

More sources

Books on the topic "Proportional odds"

1

Barbara, Bloom. Never odd or even. München: S. Schreiber, 1992.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

2

Watson, Peter. Survival analysis. Oxford University Press, 2015. http://dx.doi.org/10.1093/med:psych/9780198527565.003.0018.

Full text

Abstract:

This chapter explores survival analysis. It includes data censoring, functions of duration time (the survival function, and hazard function), Cox’s proportional hazards model, log-linearity, time varying predictors, and odds ratios.

APA, Harvard, Vancouver, ISO, and other styles

3

Elwood, Mark. Confounding. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199682898.003.0007.

Full text

Abstract:

This chapter gives the definition of confounding, a central issue in epidemiology and its dependence on two associations, with exposure and with outcome. It explains confounding in trials, cohort and case-control studies, and Simpson’s paradox. It explains the five methods of controlling confounding: restriction, randomisation, stratification, matching and multivariate methods. For randomised trials, the limits of randomisation, residual confounding, pre-stratification, intention-to-treat, management and explanatory trials, pragmatic trials are explained. It shows the Mantel–Haenszel risk ratio or odds ratio, direct and indirect standardisation, and effect modification. Frequency and individual matching, their value and limitations, over matching, confounding by indication, and calculation of matched odds ratio are shown. It explains multivariate methods, including linear, logistic, Poisson, and Cox’s proportionate hazards models, including the relationship between coefficients and odds ratios, dummy variables, conditional methods, and propensity scores.

APA, Harvard, Vancouver, ISO, and other styles

4

Peacock, Janet L., Sally M. Kerry, and Raymond R. Balise. Comparing two groups. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780198779100.003.0007.

Full text

Abstract:

Chapter 7 discusses comparing two groups, and covers graphical presentation of continuous unpaired data, the two-sample t test, and the Mann-Whitney U test. It describes the use of data transformations and how results are interpreted. It shows how to compare two proportions using the chi-squared test and how to report results as differences in proportions, relative risks, and odds ratios. It includes how to calculate 95% confidence intervals for estimates. Finally, the chapter discusses the reasons for and consequences of dichotomization of continuous data and a method for dichotomization without losing statistical power. The chapter includes analyses using Stata, SAS, SPSS, and R.

APA, Harvard, Vancouver, ISO, and other styles

5

Prout, Jeremy, Tanya Jones, and Daniel Martin. Statistical basis of clinical trials. Oxford University Press, 2014. http://dx.doi.org/10.1093/med/9780199609956.003.0009.

Full text

Abstract:

This chapter summarizes some aspects of study design and statistical analysis to allow the anaesthetist to appraise research. Types of observational study are described and aspects of interventional studies such as sample size calculation and power are explained. Research governance, phases of drug trials and levels of evidence are described. A section on statistical analysis includes expression of proportion for binary data (odds ratio, number needed to treat) and use of probability and confidence intervals to measure statistical significance.

APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "Proportional odds"

1

Wendler, Tilo. "Datenanalyse mit einem Proportional Odds Modell." In Modellierung und Bewertung von IT-Kosten, 163–86. Wiesbaden: Deutscher Universitätsverlag, 2004. http://dx.doi.org/10.1007/978-3-322-90304-4_8.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Kern, Christoph. "Proportional und partial-proportional odds Modelle zur Erklärung der Mobilitätsdisposition im Mehrebenenkontext." In Dyadische Analyse regionaler Arbeitsmarktmobilität, 73–90. Wiesbaden: Springer Fachmedien Wiesbaden, 2017. http://dx.doi.org/10.1007/978-3-658-17435-4_5.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

McNulty, Keith. "Proportional Odds Logistic Regression for Ordered Category Outcomes." In Handbook of Regression Modeling in People Analytics, 143–62. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003194156-7.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Vargas, Víctor Manuel, Pedro Antonio Gutiérrez, and César Hervás. "Deep Ordinal Classification Based on the Proportional Odds Model." In From Bioinspired Systems and Biomedical Applications to Machine Learning, 441–51. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-19651-6_43.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Pérez-Ortiz, María, Pedro Antonio Gutiérrez, Manuel Cruz-Ramírez, Javier Sánchez-Monedero, and Cesar Hervás-Martínez. "Kernelizing the Proportional Odds Model through the Empirical Kernel Mapping." In Advances in Computational Intelligence, 270–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38679-4_26.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Huang, Jian. "Maximum likelihood estimation for proportional odds regression model with current status data." In Institute of Mathematical Statistics Lecture Notes - Monograph Series, 129–45. Hayward, CA: Institute of Mathematical Statistics, 1995. http://dx.doi.org/10.1214/lnms/1215452217.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Pordeli, Pooneh, and Xuewen Lu. "A Proportional Odds Model for Regression Analysis of Case I Interval-Censored Data." In Advanced Statistical Methods in Data Science, 101–22. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-2594-5_6.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

"Proportional Odds Model." In Encyclopedia of Quality of Life and Well-Being Research, 5127. Dordrecht: Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-007-0753-5_103251.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

"Proportions, odds ratios and relative risks." In Statistical Methodologies with Medical Applications, 54–61. Chichester, UK: John Wiley & Sons, Ltd, 2017. http://dx.doi.org/10.1002/9781119258568.ch6.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

"- The Odds Ratio and Logistic Regression." In Confidence Intervals for Proportions and Related Measures of Effect Size, 252–83. CRC Press, 2012. http://dx.doi.org/10.1201/b12670-16.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Proportional odds"

1

Huang, Tingting, and Tongmin Jiang. "Design of accelerated life testing using proportional hazards-proportional odds." In 2010 Annual Reliability and Maintainability Symposium (RAMS). IEEE, 2010. http://dx.doi.org/10.1109/rams.2010.5448032.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Huang, Tingting, and Tongmin Jiang. "An extended proportional hazards-proportional odds model in accelerated life testing." In 2009 8th International Conference on Reliability, Maintainability and Safety (ICRMS 2009). IEEE, 2009. http://dx.doi.org/10.1109/icrms.2009.5270069.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Wei-Ming Yeh. "Association of the epapers and epaper confidence level using proportional odds model." In 2009 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2009. http://dx.doi.org/10.1109/icmlc.2009.5212581.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Wei-Ming Yeh. "Association of the DSLRS and DSLR Confidence level using proportional odds model." In 2009 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2009. http://dx.doi.org/10.1109/icmlc.2009.5212586.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Warti, Rini, Anang Kurnia, and Kusman Sadik. "Evaluation of Proportional Odds and Continuation Ratio Models for Smoker in Indonesia." In Proceedings of the 1st International Conference on Statistics and Analytics, ICSA 2019, 2-3 August 2019, Bogor, Indonesia. EAI, 2020. http://dx.doi.org/10.4108/eai.2-8-2019.2290483.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Li, Xingnan, Timothy D. Howard, Elizabeth J. Ampleford, Stephen P. Peters, Eugene R. Bleecker, and Deborah A. Meyers. "Genome-wide Association Study Of Asthma Severity Using Proportional Odds Model Identifies TMEM154." In American Thoracic Society 2010 International Conference, May 14-19, 2010 • New Orleans. American Thoracic Society, 2010. http://dx.doi.org/10.1164/ajrccm-conference.2010.181.1_meetingabstracts.a3728.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Sun, He, Xinling Shi, Jianhua Chen, Yajie Liu, and Yajie Liu. "Modeling and simulation for clinical trial of naratriptan based on proportional odds model." In 2012 5th International Conference on Biomedical Engineering and Informatics (BMEI). IEEE, 2012. http://dx.doi.org/10.1109/bmei.2012.6512913.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Wang, Yan, and Lu Tian. "The equivalence between Mann-Whitney Wilcoxon test and score test based on the proportional odds model for ordinal responses." In 2017 4th International Conference on Industrial Economics System and Industrial Security Engineering (IEIS). IEEE, 2017. http://dx.doi.org/10.1109/ieis.2017.8078606.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Yao, Mengjia, Qian Fu, Liuyi Gao, and Zhibin Li. "Exploring Influencing Factors to the Riding Comfort of Bicyclists on Physically Separated Bicycle Roadways in China Using Proportional Odds Model." In 11th International Conference of Chinese Transportation Professionals (ICCTP). Reston, VA: American Society of Civil Engineers, 2011. http://dx.doi.org/10.1061/41186(421)70.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Jr., J. Formisano,, and M. Lippmann. "38. Exploratory Analysis of Industrial Hygiene Data From Non-Manufacturing Facilities: Using Analysis of Variance and the Proportional Odds Model to Develop Similar Exposure Groups." In AIHce 2000. AIHA, 2000. http://dx.doi.org/10.3320/1.2763742.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Proportional odds"

1

Tangka, Florence K. L., Sujha Subramanian, Madeleine Jones, Patrick Edwards, Sonja Hoover, Tim Flanigan, Jenya Kaganova, et al. Young Breast Cancer Survivors: Employment Experience and Financial Well-Being. RTI Press, July 2020. http://dx.doi.org/10.3768/rtipress.2020.rr.0041.2007.

Full text

Abstract:

The economic burden of breast cancer for women under 50 in the United States remains largely unexplored, in part because young women make up a small proportion of breast cancer cases overall. To address this knowledge gap, we conducted a web-based survey to compare data from breast cancer survivors 18–39 years of age at first diagnosis and 40–49 years of age at first diagnosis. We administered a survey to a national convenience sample of 416 women who were 18–49 years of age at the time of their breast cancer diagnosis. We analyzed factors associated with financial decline using multivariate regression. Survivors 18–39 years of age at first diagnosis were more likely to report Stage II–IV breast cancer (P<0.01). They also quit their jobs more often (14.6%) than older survivors (4.4%; P<0.01) and faced more job performance issues (55.7% and 42.8%, respectively; P=0.02). For respondents in both groups, financial decline was more likely if the survivor had at least one comorbid condition (odds ratios: 2.36–3.21) or was diagnosed at Stage II–IV breast cancer (odds ratios: 2.04–3.51).

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!