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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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Previous issue date: 2012-07-23The 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.
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Capuano, Ana W. "Constrained ordinal models with application in occupational and environmental health." Diss., University of Iowa, 2012. https://ir.uiowa.edu/etd/2450.
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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).
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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.
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Teixeira, Juliana Cecilia da Silva. "Testes de superioridade para modelos de chances proporcionais com e sem fração de cura." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/104/104131/tde-16022018-104100/.
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Estudos que comprovem a superioridade de um fármaco em relação a outros já existentes no mercado são de grande interesse na prática clínica. Através deles a Agência Nacional de Vigilância Sanitária (ANVISA) concede registro a novos produtos, que podem curar mais rápido ou aumentar a probabilidade de cura dos pacientes, em comparação ao tratamento padrão. É de suma importância que os testes de hipóteses controlem a probabilidade do erro tipo I, ou seja, controlem a probabilidade de que um tratamento não superior seja aprovado para uso; e também atinja o poder de teste regulamentado com o menor número de indivíduos possível. Os testes de hipóteses existentes para esta finalidade ou desconsideram o tempo até que o evento de interesse ocorra (reação alérgica, efeito positivo, etc) ou são baseados no modelo de riscos proporcionais. No entanto, na prática, a hipótese de riscos proporcionais pode nem sempre ser satisfeita, como é o caso de ensaios cujos riscos dos diferentes grupos em estudo se igualam com o passar do tempo. Nesta situação, o modelo de chances proporcionais é mais adequado para o ajuste dos dados. Neste trabalho desenvolvemos e investigamos dois testes de hipóteses para ensaios clínicos de superioridade, baseados na comparação de curvas de sobrevivência sob a suposição de que os dados seguem o modelo de chances de sobrevivências proporcionais, um sem a incorporação da fração de cura e outro com esta incorporação. Vários estudos de simulação são conduzidos para analisar a capacidade de controle da probabilidade do erro tipo I e do valor do poder dos testes quando os dados satisfazem ou não a suposição do teste para diversos tamanhos amostrais e dois métodos de estimação das quantidades de interesse. Concluímos que a probabilidade do erro tipo I é subestimada quando os dados não satisfazem a suposição do teste e é controlada quando satisfazem, como esperado. De forma geral, concluímos que é imprescindível satisfazer as suposições dos testes de superioridade.Studies that prove the superiority of a drug in relation to others already existing in the market are of great interest in clinical practice. Based on them the Brazilian National Agency of Sanitary Surveillance (ANVISA) grants superiority drugs registers which can cure faster or increase the probability of cure of patients, compared to standard treatment. It is of the utmost importance that hypothesis tests control the probability of type I error, that is, they control the probability that a non-superior treatment is approved for use; and also achieve the test power regulated with as few individuals as possible. Tests of hypotheses existing for this purpose or disregard the time until the event of interest occurrence (allergic reaction, positive effect, etc.) or are based on the proportional hazards model. However, in practice, the hypothesis of proportional hazards may not always be satisfied, as is the case of trials whose risks of the different study groups become equal over time. In this situation, the proportional odds survival model is more adequate for the adjustment of the data. In this work we developed and investigated two hypothesis tests for clinical trials of superiority, based on the comparison of survival curves under the assumption that the data follow the proportional survival odds model, one without the incorporation of cure fraction and another considering cure fraction. Several simulation studies are conducted to analyze the ability to control the probability of type I error and the value of the power of the tests when the data satisfy or not the assumption of the test for different sample sizes and two estimation methods of the quantities of interest. We conclude that the probability of type I error is underestimated when the data do not satisfy the assumption of the test and it is controlled when they satisfy, as expected. In general, we conclude that it is indispensable to satisfy the assumptions of superiority tests.
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Kjellsson, Maria C. "Methodological Studies on Models and Methods for Mixed-Effects Categorical Data Analysis." Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-9333.
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Johnson, Edward P. "Applying Bayesian Ordinal Regression to ICAP Maladaptive Behavior Subscales." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd2121.pdf.
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Zingmark, Per-Henrik. "Models for Ordered Categorical Pharmacodynamic Data." Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis: Univ.-bibl. [distributör], 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-6125.
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Arnold, Nathaniel M. "Targeting the Minority: A New Theory of Diversionary Violence." Wright State University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=wright1590166439219292.
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Doussau, de Bazignan Adélaïde. "Essais cliniques de recherche de dose en oncologie : d'un schéma d'essai permettant l'inclusion continue à l’utilisation des données longitudinales de toxicité." Thesis, Paris 11, 2014. http://www.theses.fr/2014PA11T013/document.
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L’objectif des essais de phase I en oncologie est d’identifier la dose maximale tolérée (DMT). Le schéma « 3+3 » nécessite d’interrompre les inclusions en attendant l’évaluation d’une cohorte de trois patients pour définir la dose à attribuer aux patients suivants. Les investigateurs d’oncologie pédiatrique ont proposé l’adaptation Rolling 6 pour éviter cette suspension temporaire des inclusions. Dans une étude de simulation, nous avons montré qu’un schéma adaptatif avec attribution des doses basées sur un modèle statistique permettait de pallier ce problème, et identifiait plus fréquemment la DMT. Néanmoins ces trois schémas restent limités pour identifier la DMT, notamment du fait que le critère de jugement est un critère binaire, la survenue de toxicité dose-limitante sur un cycle de traitement. Nous avons proposé un nouveau schéma adaptatif utilisant les données ordinales répétées de toxicité sur l’ensemble des cycles de traitement. La dose à identifier est celle associée au taux de toxicité grave maximal par cycle que l’on juge tolérable. Le grade maximal de toxicité par cycle de traitement, en 3 catégories (grave / modéré / nul), a été modélisé par le modèle mixte à cotes proportionnelles. Le modèle est performant à la fois pour détecter un effet cumulé dans le temps et améliore l’identification de la dose cible, sans risque majoré de toxicité, et sans rallonger la durée des essais. Nous avons aussi étudié l’intérêt de ce modèle ordinal par rapport à un modèle logistique mixte plus parcimonieux. Ces modèles pour données longitudinales devraient être plus souvent utilisés pour l’analyse des essais de phase I étant donné leur pertinence et la faisabilité de leur implémentationPhase I dose-finding trials aim at identifying the maximum tolerated dose (MTD). The “3+3” design requires an interruption of enrolment while the evaluation of the previous three patients is pending. In pediatric oncology, investigators proposed the Rolling 6 design to allow for a more continuous enrollment. In a simulation study, we showed that an adaptive dose-finding design, with dose allocation guided by a statistical model not only minimizes accrual suspension as with the rolling 6, and but also led to identify more frequently the MTD. However, the performance of these designs in terms of correct identification of the MTD is limited by the binomial variability of the main outcome: the occurrence of dose-limiting toxicity over the first cycle of treatment. We have then proposed a new adaptive design using repeated ordinal data of toxicities experienced during all the cycles of treatment. We aim at identifying the dose associated with a specified tolerable probability of severe toxicity per cycle. The outcome was expressed as the worst toxicity experienced, in three categories (severe / moderate / no toxicity), repeated at each treatment cycle. It was modeled through a proportional odds mixed model. This model enables to seek for cumulated toxicity with time, and to increase the ability to identify the targeted dose, with no increased risk of toxicity, and without delaying study completion. We also compared this ordinal model to a more parsimonious logistic mixed model.Because of their applicability and efficiency, those models for longitudinal data should be more often used in phase I dose-finding trials
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Minetree, Jennifer Grace. "Interspaces: The Odd Fellows Lodge." Thesis, Virginia Tech, 1997. http://hdl.handle.net/10919/36969.
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A discussion concerning the frame, foundation, midsection and stair of the Odd Fellows Lodge. Influenced by the precedents of rural Virginia's pragmatic building techniques and the personal and poetic work of architect Carlo Scarpa, the interventions to the Lodge maintain its gentle existence while asserting a new presence as an introduction to downtown Blacksburg.Master of Architecture
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Fernandes, Alfredo Manoel da Silva. "Duração da hospitalização e faturamento das despesas hospitalares em portadores de cardiopatia congênita e de cardiopatia isquêmica submetidos à intervenção cirúrgica cardiovascular assistidos no protocolo da via rápida." Universidade de São Paulo, 2003. http://www.teses.usp.br/teses/disponiveis/5/5131/tde-21072014-110315/.
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Com o objetivo de avaliar o atendimento dos pacientes submetidos à intervenção cirúrgica cardiovascular no protocolo de atendimento na via rápida (fast track recovery) em relação ao protocolo convencional, foi comparada a movimentação dos pacientes atendidos em ambos os protocolos nas diferentes unidades hospitalares. O estudo foi realizado em hospital público universitário especializado em cardiologia de 400 leitos, de referência terciária para o Sistema Único de Saúde. Foram estudados 175 pacientes, 107 (61%) homens e 68 (39%) mulheres, de idades entre 2 meses a 81 anos, dos quais 107 operados no protocolo da via rápida e 68 no protocolo convencional. Foram avaliadas variáveis demográficas, clínicas e, para avaliar a movimentação dos pacientes nas diferentes unidades hospitalares, as taxas de alta por unidade de tempo em cada unidade. A análise estatística foi feita por meio de análise exploratória, método de Kaplan Meier e modelo de riscos proporcionais de Cox. A análise de variância foi empregada para comparar o faturamento das despesas. A taxa de alta das diferentes unidades hospitalares por unidade de tempo dos portadores de cardiopatia congênita atendidos no protocolo da via rápida em relação ao protocolo da via convencional foi: a) 11,3 vezes a taxa de alta quando assistidos no protocolo na via convencional quanto ao tempo de permanência no centro cirúrgico; b) 6,3 vezes quanto à duração da intervenção cirúrgica; c) 6,8 vezes quanto à duração da anestesia; d) 1,5 vezes quanto à duração da perfusão; e) 2,8 vezes quanto à permanência na unidade de recuperação pós-operatória I; f) 6,7 vezes quanto à duração da hospitalização; g) 2,8 vezes quanto à permanência na unidade de internação pré-operatória; h) 2,1 vezes quanto à permanência na unidade de internação após a alta da unidade de terapia intensiva de recuperação pós-operatória. Para os portadores de cardiopatia isquêmica, as taxas de alta das unidades hospitalares para os protocolos de atendimento no protocolo da via rápida e no protocolo convencional não demonstraram diferença estatisticamente significante. Os valores de faturamento das despesas de internação dos portadores de cardiopatia congênita decorrentes de exames e procedimentos realizados nas fases pré- e pós-operatória e dos exames da fase trans-operatória foram menores quando os pacientes foram assistidos no protocolo da via rápida. Portanto, os portadores de cardiopatias congênita apresentaram menor permanência hospitalar nos recursos médicos hospitalares instalados, quando assistidos no protocolo de atendimento na via rápida, bem como menores despesas nas fases pré- e pós- operatória da internação.Objective - To evaluate patient assistance in pre, per and postoperative phases of cardiac surgical intervention under fast track recovering protocol compared to the conventional way. Patients - 175 patients were studied, 107 (61%) men and 68 (39%) women. Ages 2 months to 81 years old. Patients included: first surgical intervention, congenital and ischemic cardiopathy without complexity, normal ventricular function and with at least 2 preoperative ambulatory consultations. Patients submitted to emergency surgeries were excluded. Interventions - assistance submitted by fast track and conventional protocol. Statistical analysis (measures) - exploratory, uni-varied (Kaplan Meier) and multi-varied (Cox) of the time in each admission unit. Hospital installations were classified in ambulatory, preoperative admission unit, surgical center, postoperative recovery unit and postoperative admission unit; the expression of this use was the discharge rate by unit of time from the significant interaction observed between assistance protocol and the kind of cardiopathy for the stay in the surgical center, surgical intervention time, stay in postoperative recovery unit, anesthesia time and time between admission and surgery dates. Results - the patients of congenital cardiopathy who underwent the protocol of conventional way recovery in relation to the fast track protocol, in the reliability range of 95% allows one to state that discharge rate by unit of time of the congenital cardiopathy patients assisted by the fast track protocol was: 11.3 times the discharge rate when assisted by the conventional way protocol as to the time of staying in the surgical center; 6.3 times as to the duration of the surgical intervention; 6.8 times as to duration of the anesthesia; 1.5 times as to the duration of the perfusion; 2.8 times as to the stay in the postoperative recovery unit; 6.7 times as to the stay in the hospital (period of time between the admission and the discharge date); 2.8 times as to the stay in the preoperative admission unit ( period of time between the admission date and the surgery date); 2.1 times as to the stay in the postoperative unit (period of time between the date of leaving the postoperative recovery unit and the date of discharge from the hospital). For the ischemia cardiopathy patients the risks concerning the protocols of recovery by the traditional way and the fast track were the same. CONCLUSIONS - The data concerning this study allows one to suggest that the assistance can be more efficient if one takes into consideration some variables studied in the protocol of fast track recovery. The congenital and ischemic cardiopathy patients presented shorter interval of time (concerning hospital stay in doctor-hospital installed facilities) when assisted in the fast track recovery protocol as well as fewer expenses with medical and hospital assistance.
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黃翊恆. "Monitoring Profiles based on Proportional Odds Models." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/96449266420016990710.
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LAI, YU-HSUAN, and 賴毓宣. "Proportional Hazard Model and Proportional Odds Model under Dependent Current Status Data." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/z843bh.
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碩士國立中正大學
數學系統計科學研究所
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This thesis focuses on the estimation of the parameter under the regression model on current status data with dependent censoring, using proportional hazard model and proportional odds model to analyze the data. We consider the failure time to be related to the observation time, so we use Archimedean Copula to specify the dependency. According to Hsieh and Chen (2014), we can estimate the survival function of the failure time. We consider a general regression model and construct two proposed methods to obtain the parameter. Then, examine the finite sample performance of the proposed estimation procedures via simulation studies. Finally, we apply our proposed methodologies to analyze the tumorigenicity data on mice.
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Chuang, Ya Chu, and 莊雅筑. "Profile monitoring based on additive proportional odds models." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/63168956068948857327.
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Zhang, Hao. "Modeling and planning accelerated life testing with proportional odds." 2007. http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.13840.
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Zhang, Yan-Sheng, and 張晏昇. "Evaluating the Proportional Odds Assumption with CurrentStatus Survival Data." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/83897070541970033455.
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碩士淡江大學
數學學系碩士班
101
Regression diagnostic problems have been extensively studied for complete or right-censored survival data but so less for current status survival data. Here the current status survival data include an examination time, an status indicator for whether or not the failure has occurred by the examination time, and covariates. In this thesis, we have established four methods for evaluating the proportional odds assumption of current status survival data. They are the log odds curve plots, the observed and expected survival curve plots, the Cox-Snell residual method, and the Brier-score method. Also, the corresponding goodness-of-fit indices for four methods are proposed. Simulation results reveal good performance of the proposed methods and three real examples illustrate the applications of the proposed methods.
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Liu, Mei-Fang, and 劉美芳. "Semiparametric Estimation in Proportional Odds Models with missing covariate data." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/44702054578784457559.
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Liu, Ching-Hung, and 劉慶鴻. "Analysis of Current Status Data under Proportional Odds Cure Model." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/08659299216992154911.
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碩士淡江大學
數學學系碩士班
100
Survival data with a cured subgroup have been extensively studied in the context of right-censored data but less for current status data. Motivated by the cataract dataset from a cross-sectional study, where the occurrences of cataract was current status censored and a fraction of subjects seemed not susceptible to cataract, we describe a maximum likelihood method for analyzing current status data with a cured subgroup. Some regression approaches based on current status data with a cured subgroup have been developed, Kuk and Chen (1992); Tsodikov (2003); Lu and Ying (2004), but all under two-component mixture cure models (Berkson and Gage (1952)). Alternatively, we consider the proportional odds cure model (Zeng et al (2006)), a non-mixture model, in this study. To ensure identifiability of the model, in the estimation, we assume the study unit who has the largest censoring time among all right censored observations is cured. We evaluate the proposed method through simulation studies and illustrate it with the cataract data.
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Chen, Yu-Chieh, and 陳禹捷. "Applications of an extended proportional odds model for survival regression." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/98621516341682577035.
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Ho, Sen-Chieh, and 何森傑. "Estimation in Proportional Odds Models with measurement error covariate data." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/40022697451878361568.
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碩士逢甲大學
統計與精算所
92
The aim of this paper is to make inferences of the estimates in proportional odds model with the covariates that are subject to measurement errors. We extend the ideas of refined regression calibration (RRC) in Liang and Liu（1991）and of sufficiency score(SS) estimate and conditional score(CS) estimate in Stefanski and Carroll(1985， 1987) with logistics regression model. We also use naive estimate that substitute the surrogate with measurement errors for the unknown covariates in unbiased estimating function. We use a simulation study to characterize the performance of these methods. According to the results of simulation, naive estimate give rise to serious bias, the good performance property of RRC method will hold for the covariates with normal distribution and both SS and CS perform better when the regression coefficient is large. Besides,the performance of SS and CS is obviously better than that of RRC when covariates follow chi-square distribution.
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Chang, Kai lan, and 張凱嵐. "Bayesian semiparametric proportional odds models for spatially correlated survival data." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/64624759019123246779.
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碩士國立政治大學
統計研究所
97
The databases of Geographic Information System (GIS) have gained attention among different fields of statisticians to develop and analyze models which account for spatial clustering and variation. There is an emerging interest in modeling spatially correlated survival data in public health and epidemiologic studies. In this article, we develop Bayesian multivariate semiparametric hierarchical models to incorporate both spatially correlated and uncorrelated frailties to answer the question of spatial variation in the survival patterns, and we use multivariate conditionally autoregressive (MCAR) model to detect that whether there exists the spatial cluster across different areas. The baseline hazard function will be modeled semiparametrically using mixtures of finite Polya trees. The SEER (Surveillance Epidemiology and End Results) database from the National Cancer Institute (NCI) provides comprehensive cancer data about patient’s survival time, regional information, and others demographic information. We implement our Bayesian hierarchical spatial models on Iowa cancer data extracted from SEER database. We illustrate how to compute the conditional predictive ordinate (CPO), the average log-marginal pseudo-likelihood (ALMPL), and deviance information criterion (DIC), which are Bayesian criterions for model checking and comparison among competing models.
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Hsieh, Jing-Ru, and 謝靜茹. "The imputation approach for proportional hazardmodel, proportional odds model, and quantileregression model under semi-competing risks data." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/13515631389363761842.
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碩士國立中正大學
數學系統計科學研究所
101
This thesis focues on the analysis of the proportional hazard model, proportional odds model and quantile regression model under semi-competing risks data. Without extra assumptions, we can not make inference on the non-terminal event because the non-terminal event may be dependently censored by the terminal event. Thus, we use the Archimedean copula model to specify the dependency between the non-terminal event time and the terminal event time. Under the Archimedean copula model assumption, we adopt the mean imputation method and the median imputation method to impute the non-terminal event time and use the standard method to estimate the regression coecients for each model. We examine the nite-sample performance of the proposed approaches by simulation studies. We also apply our suggested approachs to analyze the Bone Marrow Transplant data.
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Ni, Yu-Cheng, and 倪裕程. "Bayesian Survial Analysis for Proportional Odds Model with Current Status Data." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/22754508032061777184.
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碩士中原大學
應用數學研究所
100
The mainly discuss to estimate parameters from proportional odds model with current status data by bayesian survival analysis in this paper. Current Status data is an interval censored data. The observation only include the examination time and the failure time is larger than examination time or not. For example, suppose a study is conducted to measure the impact of smoking cigarette on lung cancer. Let the covariate Z=0 mean no smoking and Z=1 mean smoking. And then check the subject got cancer or not at the examination time. We choose proportional odds model in this paper. We consider the proportional odds function that is a continuous function, but the NPMLE can only show us a step function in small sample. Thus we use the bernstein polynomial to estimate a smooth function.
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Wu, Meng-Chian, and 吳孟倩. "Bayesian Survival Analysis for Proportional Odds Model with Right Censored Data." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/13737583359684640980.
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碩士中原大學
數學研究所
100
The purpose of this paper is the proportional odds model in the right censored data to go on estimated parameters , that using Bayesian methodsand observe the its conformance. in survival analysis of the data collection, often survival time (survival time) or the situation of the incident could not be fully recorded. Because end of the study (end of the study) or lost of following up (loss of following up) case, the incident could not be observed, which we call right the censored (right the censored), then right the censored data (right the censored data) observations only include tracking ,but also time X and time T incident within a set limit before the time C. We believe that the proportional odds function as a kind of continuous function, the KM method estimates the status quo data survival function that only get a step function, and in the number of samples is great in order to estimated smooth curve, we proposed that the Bernstein polynomials to estimate, because Bernstein polynomial easy to take into account the geometric information, and a smaller number of samples to estimate a smooth curve. Of this paper: Section II describes the Bernstein polynomial coefficients and graphics. Section III presents the status quo data types. Section IV to derive the likelihood function and Bayesian inference. Section V for the algorithm. Section VI for the simulation.
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Ke, Hsing-Chen, and 柯興杰. "Likelihood Ratio Test for Proportional Odds Model with Current Status Data." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/80581952057372650416.
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碩士淡江大學
數學學系碩士班
99
Current status data result from a simple form of interval censoring in which the observation consists only of an examination time and knowledge of whether the failure time has occurred before the exam. Semiparametric regression methods which examine the relationship between the failure time and covariates have been studied extensively for current status data. In this thesis, we consider the likelihood ratio test for testing covariate effect under the proportional odds model with current status data and propose an easily implemented algorithm for computing the statistics. The algorithm proposed is based on a set of self-consistency equations and its convergence is proved by contraction principle. The adequacy of the Chi-squared approximation for likelihood ratio statistics and the availability of the algorithm are demonstrated in simulation studies and in the analyses of two real data.
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Lei, Kuen-Yuan, and 雷坤原. "Bayesian Analysis of Right Censored Dataunder Bernstein Proportional Odds Cure Model." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/rzv2nu.
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碩士中原大學
應用數學研究所
102
His thesis is about the process of biomedical research often collect data and time-related cases of long-term follow. Survival data with a cured subgroup have been extensively studied in the context of right-censored data . We apply Bayesian method and M.C.M.C. algorithm to conduct data analysis and estimation cured subgroups right cencored status, respectively. ,it performs great when manipulating calculation.
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Chang, Hsiu Che, and 張修澤. "Bayesian Analysis of Current Status Data under Bernstein Proportional Odds Cure Model." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/g65533.
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碩士中原大學
應用數學研究所
102
The main concept of this thesis is the research of analysis of survival data of cured subgroups’current status. We apply Bayesian method and M.C.M.C. algorithm to conduct data analysis and estimation, respectively. In this thesis, we provide two algorithms; one is increasing algorithm, and the other is concave-downward-increasing algorithm, to compare the results. It is faster and of higher accuracy to conduct the estimation with the later if the real function is increasing and concave downwards. Besides, it performs great when manipulating calculation.
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Ku, Pai-Fu, and 顧百芙. "An MSEWMA Control Chart for Monitoring Proportional Odds Profiles under Small Samples." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/5833md.
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Yang, Fang-Chen, and 楊芳甄. "Sample Size Calculation for the Proportional Odds Model with Current Status Data." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/yf4m6r.
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碩士淡江大學
數學學系數學與數據科學碩士班
106
In the study of current status data, when the failure of interest examined once, the available information are the examination time and the indicator of the failure has occurred or not by the examination time. Willamson, Lin and Kim (2009) proposed a sample size formula for current status data under the proportional hazards model with Weibull baseline distribution. However, a current status sample is not necessary to follow the proportional hazards model or satisfy the Weibull baseline assumption. In this thesis we consider another popular survival model, the proportional odds model, with a flexible baseline distribution (Sparling et al. 2006) includes Weibull, Log-Logistic. Log-Normal distributions. Under this model, we proposed a sample size formula for current status data. Simulations certify the validity of the formula, two real examples illustrate the applications of the method.
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Liu, Mao-Ting, and 劉懋婷. "Maximum Likelihood Estimator of Proportional Odds Modelwith Right Censored Data Using Bernstein Polynomials." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/40438995626699915385.
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碩士中原大學
應用數學研究所
101
The thesis focused on adding covariates to the right censored data with Proportional Odds Model, refer to Pettiet(1982) and Bennett(1983a,b) for more detail. For survival analysis, the semi-parametric regression has been widely used to calculate the correlation of covariate Z and the failure time T, such as the proportional hazards model and proportional odds model. The proportional odds model was also taken by Wu (2012) to calculate the nonparametric estimators per Bernstein polynomials,Bayesian Methodology and Makov Chain Monte Carlo (M.C.M.C.). The model taken byWu is used for this thesis as well to get the nonparametric estimator by maximum likelihood estimation (M.L.E.) and M.C.M.C.. We proved the model workable with excellent results by simulations.
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Chuang, Ya-Wen, and 莊雅雯. "Maximum Likelihood Estimator of Proportional Odds Model withCurrent Status Data Using Bernstein Polynomials." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/27326391726701645672.
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碩士中原大學
應用數學研究所
101
In this paper, we use the status quo data proportional odds model (see Bennett (1983a, b)) to carry out research in the Ke Xingjie’s Thesis (2011), he used the maximum likelihood estimation method and the use of proportional odds ratio model, research data likelihood ratio test statistic. In Ni Yu Cheng’s (2012) paper is to come and go with the Bayesian approach to estimate, but only estimated the proportional odds model, and propose a Bernstein polynomial used in Bayesian survival analysis, this thesis the model assumptions and Ni Yu Cheng (2012) paper is the same, but the estimated parameters, we are using the maximum likelihood estimation method. The nonparametric parameters of the model part, as with Bernstein polynomials in the covariate Z discrete value of 0 or 1, with the Markov chain Monte Carlo method to find the parameters of maximum likelihood estimation (MLE). And simulation, we have a good performance.
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Tai, Wen-Chieh, and 戴文傑. "Maximum Likelihood Estimator of Current Status Data under Bernstein Proportional Odds Cure Model." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/sdg937.
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碩士中原大學
應用數學研究所
102
In this paper, we use Maximum Likelihood Estimator of Current Status Data under Bernstein Proportion Odds Cure Model to carry out research under Ching-Hung Liu’s Thesis (2012) , using the Weibull distribution survival distribution of time to do, is to use the estimated parameters maximum likelihood estimator, we use Bernstein polynomials to replace the Weibull distribution, the estimated parameters are also using maximum likelihood estimator. In making the maximum likelihood estimate, because many parameters and we are not fixed, so we will use the Monte Carlo Markov chain (M.C.M.C.) to calculate the maximum likelihood estimator, and the potential to make a distribution function graphic factors, write another basis for the comparison of algorithms and general graphics algorithms that map looks like if found, then use a graphical algorithm which estimates the optimum.
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Liu, Chien-Shin, and 劉建鑫. "Use of the proportional odds model to analyze disease severity estimation data for comparing treatments." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/sef5yz.
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碩士國立中興大學
農藝學系所
106
In the field of plant epidemiology, plant diseases severity generally needs to be assessed. Severity may be defined as the area of plant tissue affected by disease and may be expressed as quantitative data such data can be used to do research to predict yield loss, to compare treatments, and for monitoring and forecasting of plant diseases. The purpose of this study is to evaluate the performance of the proportional odds model in estimating disease severity for the purpose of comparing treatments (e.g., varieties, fungicides, etc.). A simulation method was employed to execute the study. The parameters of the simulation were estimated using the original data from the field based on a sample of citrus canker (Xanthomonas citri subsp. Citri)-infected grapefruit leaves. The proportional odds model was compared with the method using midpoint conversions of ordinal intervals. Here, the criteria for comparison is the power of hypothesis testing. The results of this study show that, at low disease severity (≤30%), the performance of the proportional odds model is superior to that of the midpoint conversion of the interval. However, as disease severity increases, the power of hypothesis testing by using the proportional odds model decreases. As the actual disease severity approaches 30%, the advantage of the proportional odds model will gradually disappear although it is still slightly better than using the categorical type of amended 10% scale, but is not better than the Horsfall-Barratt (H-B) scale. We hope that the results of this study will be helpful in improving the accuracy of disease severity assessment in plant epidemiology.
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CHAO, MING-CHE, and 趙明哲. "The Application of Partial Proportional Odds Model in Taiwan: Ambulance Crashes and Novice Motorcyclist Crashes." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/6nck8y.
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博士國立暨南國際大學
土木工程學系
106
Motorcycles comprised over 60% of motor vehicles in Taiwan. There were still many motorcycle crashes in Taiwan especially for the young riders. Medical emergency vehicles help accident victims get to the hospital quickly. However, there have been an increasing number of ambulance crashes on the roads of Taiwan in the last decade. This study investigated the characteristics of both novice motorcyclist crashes and ambulance crashes in Taiwan. The multinomial logit (MNL) model, ordered logit (OL) model, and partial proportional odds (PPO) model were investigated for the relationships between the severity of novice motorcyclist crashes or ambulance crashes and their potential risk factors. For novice motorcyclist crashes, various risk factors have different effects on the severity level, such as the rider’s characteristics, licensing conditions and the environment. The novice rider who was under age or unlicensed had higher probability in the fatal crashes. For the ambulance crashes, when another car was involved in ambulance crashes, there was a disproportionate effect on the overall severity predicted by the PPO model. The male ambulance drivers and car drivers who may fail to yield to the ambulance had a higher risk of severe injury from ambulance crashes. Novice motorcyclist crashes and ambulance crashes are happening issues in Taiwan traffic safety which need further policy adjustments and public education.
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Chen, Yan-Ling, and 陳彥伶. "Investigating Impact Factors for Comparing Treatments of Plant Disease Severities Using a Proportional Odds Model." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/xuz4za.
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碩士國立中興大學
農藝學系所
106
Disease severity assessment is an important issue in plant epidemiology. The purposes of disease severity assessment are to forecast the yield loss, to compare the effects of treatments, and to evaluate the resistance of plants breeding. Severity is often used to estimate disease intensity. The definition of disease severity is the nearest percentage of lesions of all the area on the leaf. Because human’s eyes can’t easily discriminate disease severities, it is difficult to get the estimated values; also it will take more time to assess severities. Therefore, it is necessary to use the score of the categorical scale to estimate the severities. Nowadays, most previous studies have used the transformation of the midpoint of the interval scale to represent the corresponding interval. Furthermore, as two treatments compared, t-test of statistical methods is performed. When t-test is used, the assumption is the populations we are sampling have the shape of normal distributions. Otherwise, it will violate the assumption of t-test. However, in real life, there are many types of different data. There are not all data sampling from normal distributions. Base on the reasons, we used proportional odds model in this study to compare the difference of two treatments. The criterion of comparisons is the power of hypothesis testing. Our results show that, as the disease severity is ≤40%, this method of the proportional odds model is not inferior to the methods of the midpoint conversion. Even, there is a superior result by using proportional odds model, when there is a larger variation for the mean severity of the data ≤10%. Moreover, some impact factors affect the accuracy of estimates. For example, the variation of the data, actual disease severity, different categorical scale, computation of the estimated value, and the methods of hypothesis testing. Finally, the results of the study will be helpful for analyzing the data in agriculture science.
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Haddadian, Rojiar. "Simulation-based estimation in regression models with categorical response variable and mismeasured covariates." 2016. http://hdl.handle.net/1993/31535.
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A common problem in regression analysis is that some covariates are measured with errors. In this dissertation we present simulation-based approach to estimation in two popular regression models with a categorical response variable and classical measurement errors in covariates. The first model is the regression model with a binary response variable. The second one is the proportional odds regression with an ordinal response variable.
In both regression models we consider method of moments estimators for therein unknown parameters that are defined via minimizing respective objective functions. The later functions involve multiple integrals and make obtaining of such estimators unfeasible. To overcome this computational difficulty, we propose Simulation-Based Estimators (SBE). This method does not require parametric assumptions for the distributions of the unobserved covariates and error components. We prove consistency and asymptotic normality of the proposed SBE's under some regularity conditions. We also examine the performance of the SBE's in finite-sample situations through simulation studies and two real data sets: the data set from the AIDS Clinical Trial Group (ACTG175) study for our logistic and probit regression models and one from the Adult Literacy and Life Skills (ALL) Survey for our regression model with the ordinal response variable and mismeasured covariates.October 2016