Dissertations / Theses: 'Cox proportional hazards regression'

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Persson, Inger. "Essays on the Assumption of Proportional Hazards in Cox Regression." Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis : Univ.-bibl. [distributör], 2002. http://publications.uu.se/theses/91-554-5208-6/.

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Crumer, Angela Maria. "Comparison between Weibull and Cox proportional hazards models." Kansas State University, 2011. http://hdl.handle.net/2097/8787.

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Master of Science
Department of Statistics
James J. Higgins
The time for an event to take place in an individual is called a survival time. Examples include the time that an individual survives after being diagnosed with a terminal illness or the time that an electronic component functions before failing. A popular parametric model for this type of data is the Weibull model, which is a flexible model that allows for the inclusion of covariates of the survival times. If distributional assumptions are not met or cannot be verified, researchers may turn to the semi-parametric Cox proportional hazards model. This model also allows for the inclusion of covariates of survival times but with less restrictive assumptions. This report compares estimates of the slope of the covariate in the proportional hazards model using the parametric Weibull model and the semi-parametric Cox proportional hazards model to estimate the slope. Properties of these models are discussed in Chapter 1. Numerical examples and a comparison of the mean square errors of the estimates of the slope of the covariate for various sample sizes and for uncensored and censored data are discussed in Chapter 2. When the shape parameter is known, the Weibull model far out performs the Cox proportional hazards model, but when the shape parameter is unknown, the Cox proportional hazards model and the Weibull model give comparable results.

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Sasieni, Peter D. "Beyond the Cox model : extensions of the model and alternative estimators /." Thesis, Connect to this title online; UW restricted, 1989. http://hdl.handle.net/1773/9556.

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Hughes, James A. "Person, environment, and health and illness factors influencing time to first analgesia and patient experience of pain management in the adult emergency department." Thesis, Queensland University of Technology, 2018. https://eprints.qut.edu.au/123311/3/James_Hughes_Thesis.pdf.

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This thesis explored patient, clinician, environmental and illness factors that influence how doctors and nurses treat patients who present to the emergency department in pain. The findings confirm that patients are more likely to receive analgesic medication in a shorter time and have a more positive experience with pain care when the emergency department is less busy, they have less pre-existing illness, and have a higher socioeconomic status. The identification of these factors has important implications for making changes to the way emergency departments and emergency clinicians treat pain in a timely and patient-centered manner.

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Calsavara, Vinícius Fernando. "Estimação de efeitos variantes no tempo em modelos tipo Cox via bases de Fourier e ondaletas Haar." Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-26082015-140547/.

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O modelo semiparamétrico de Cox é frequentemente utilizado na modelagem de dados de sobrevivência, pois é um modelo muito flexível e permite avaliar o efeito das covariáveis sobre a taxa de falha. Uma das principais vantagens é a fácil interpretação, de modo que a razão de riscos de dois indivíduos não varia ao longo do tempo. No entanto, em algumas situações a proporcionalidade dos riscos para uma dada covariável pode não ser válida e, este caso, uma abordagem que não dependa de tal suposição é necessária. Nesta tese, propomos um modelo tipo Cox em que o efeito da covariável e a função de risco basal são representadas via bases de Fourier e ondaletas de Haar clássicas e deformadas. Propomos também um procedimento de predição da função de sobrevivência para um paciente específico. Estudos de simulações e aplicações a dados reais sugerem que nosso método pode ser uma ferramenta valiosa em situações práticas em que o efeito da covariável é dependente do tempo. Por meio destes estudos, fazemos comparações entre as duas abordagens propostas, e comparações com outra já conhecida na literatura, onde verificamos resultados satisfatórios.
The semiparametric Cox model is often considered when modeling survival data. It is very flexible, allowing for the evaluation of covariates effects. One of its main advantages is the easy of interpretation, as long as the rate of the hazards for two individuals does not vary over time. However, this proportionality of the hazards may not be true in some practical situations and, in this case, an approach not relying on such assumption is needed. In this thesis we propose a Cox-type model that allows for time-varying covariate effects, for which the baseline hazard is based on Fourier series and wavelets on a time-frequency representation. We derive a prediction method for the survival of future patients with any specific set of covariates. Simulations and an application to a real data set suggest that our method may be a valuable tool to model data in practical situations where covariate effects vary over time. Through these studies, we make comparisons between the two approaches proposed here and comparisons with other already known in the literature, where we verify satisfactory results.

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Thapa, Ram. "Modeling Mortality of Loblolly Pine Plantations." Diss., Virginia Tech, 2014. http://hdl.handle.net/10919/46726.

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Accurate prediction of mortality is an important component of forest growth and yield prediction systems, yet mortality remains one of the least understood components of the system. Whole-stand and individual-tree mortality models were developed for loblolly pine plantations throughout its geographic range in the United States. The model for predicting stand mortality were developed using stand characteristics and biophysical variables. The models were constructed using two modeling approaches. In the first approach, mortality functions for directly predicting tree number reduction were developed using algebraic difference equation method. In the second approach, a two-step modeling strategy was used where a model predicting the probability of tree death occurring over a period was developed in the first step and a function that estimates the reduction in tree number was developed in the second step. Individual-tree mortality models were developed using multilevel logistic regression and survival analysis techniques. Multilevel data structure inherent in permanent sample plots data i.e. measurement occasions nested within trees (e.g., repeated measurements) and trees nested within plots, is often ignored in modeling tree mortality in forestry applications. Multilevel mixed-effects logistic regression takes into account the full hierarchical structure of the data. Multilevel mixed-effects models gave better predictions than the fixed effects model; however, the model fits and predictions were further improved by taking into account the full hierarchical structure of the data. Semiparametric proportional hazards regression was also used to develop model for individual-tree mortality. Shared frailty model, mixed model extension of Cox proportional hazards model, was used to account for unobserved heterogeneity not explained by the observed covariates in the Cox model.
Ph. D.

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Morgan, Jerry R. "A study of promotion and attrition of mid-grade officers in the U.S. Marine Corps : are assignments a key factor? /." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2005. http://library.nps.navy.mil/uhtbin/hyperion/05Mar%5FMorgan%5FJerry.pdf.

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Sauls, Beverly J. "Relative Survival of Gags Mycteroperca microlepis Released Within a Recreational Hook-and-Line Fishery: Application of the Cox Regression Model to Control for Heterogeneity in a Large-Scale Mark-Recapture Study." Scholar Commons, 2013. http://scholarcommons.usf.edu/etd/4940.

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The objectives of this study were to measure injuries and impairments directly observed from gags Mycteroperca microlepis caught and released within a large-scale recreational fishery, develop methods that may be used to rapidly assess the condition of reef fish discards, and estimate the total portion of discards in the fishery that suffer latent mortality. Fishery observers were placed on for-hire charter and headboat vessels operating in the Gulf of Mexico from June 2009 through December 2012 to directly observe reef fishes as they were caught by recreational anglers fishing with hook-and-line gear. Fish that were not retained by anglers were inspected and marked with conventional tags prior to release. Fish were released in multiple regions over a large geographic area throughout the year and over multiple years. The majority of recaptured fish were reported by recreational and commercial fishers, and fishing effort fluctuated both spatially and temporally over the course of this study in response to changes in recreational harvest restrictions and the Deepwater Horizon oil spill. Therefore, it could not be assumed that encounter probabilities were equal for all individual tagged fish in the population. Fish size and capture depth when fish were initially caught-and-released also varied among individuals in the study and potentially influenced recapture reporting probabilities. The Cox proportional hazards regression model was used to control for potential covariates on both the occurrence and timing of recapture reporting events so that relative survival among fish released in various conditions could be compared. A total of 3,954 gags were observed in this study, and the majority (77.26%) were released in good condition (condition category 1), defined as fish that immediately submerged without assistance from venting and had not suffered internal injuries from embedded hooks or visible damage to the gills. However, compared to gags caught in shallower depths, a greater proportion of gags caught and released from depths deeper than 30 meters were in fair or poor condition. Relative survival was significantly reduced (alpha (underline)<(/underline)0.05) for gags released in fair and poor condition after controlling for variable mark-recapture reporting rates for different sized discards among regions and across months and years when individual fish were initially captured, tagged and released. Gags released within the recreational fishery in fair and poor condition were 66.4% (95% C.I. 46.9 to 94.0%) and 50.6% (26.2 to 97.8%) as likely to be recaptured, respectively, as gags released in good condition. Overall discard mortality was calculated for gags released in all condition categories at ten meter depth intervals. There was a significant linear increase in estimated mortality from less than 15% (range of uncertainty, 0.1-25.2%) in shallow depths up to 30 meters, to 35.6% (5.6-55.7%) at depths greater than 70 meters (p < 0.001, R2 = 0.917). This analysis demonstrated the utility of the proportional hazards regression model for controlling for potential covariates on both the occurrence and timing of recapture events in a large-scale mark-recapture study and for detecting significant differences in the relative survival of fish released in various conditions measured under highly variable conditions within a large-scale fishery.

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Lindberg, Erik. "A study of the effect of inbreeding in Skellefteå during the 19th century : Using Cox Proportional hazard model to analyze lifespans and Poisson/Negative Binomial regression to analyze fertility." Thesis, Umeå universitet, Statistik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-122687.

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Inbreeding is defined as when two individuals who are related mate and produce offspring. The level of inbreeding for an individual can be determined by calculating an inbreeding coefficient. Inbreeding can enhance both positive and negative traits. The risk for recessive diseases also increase. Data from old church records from the region of Skellefteå covering individuals from the late 17th century to the early 20th century has been made available. From this data parent-child relations can be observed and levels of inbreeding calculated. By analyzing the available data using Cox Proportional Hazard regression model it was shown that the level inbreeding affected the lifespan of an individual negatively if the parents are second cousins or more closely related. Using Poisson- and Negative Binomial regression, no evicence of an effect of inbreeding of fertility could be found.

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Danardono. "Multiple Time Scales and Longitudinal Measurements in Event History Analysis." Doctoral thesis, Umeå : Dept. of Statistics, Umeå Univ, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-420.

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Frazão, Italo Marcus da Mota. "Modelos com sobreviventes de longa duração paramétricos e semi-paramétricos aplicados a um ensaio clínico aleatorizado." Universidade de São Paulo, 2012. http://www.teses.usp.br/teses/disponiveis/11/11134/tde-13032013-093628/.

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Diversos modelos têm sido propostos na literatura com o objetivo de analisar dados de sobrevivência em que a população sob estudo é assumida ser uma mistura de indivíduos suscetíveis (em risco) e não suscetíveis a um específico evento de interesse. Tais modelos são usualmente denominados modelos com sobreviventes de longa duração ou modelos com fração de cura. Neste trabalho, diversos desses modelos (nos contextos paramétrico e semi-paramétrico) foram considerados para analisar os dados de um ensaio clínico aleatorizado conduzido com o objetivo de comparar três estratégias terapêuticas (cirurgia, angioplastia e medicamentoso) utilizadas no tratamento de pacientes com doença coronariana multiarterial. Em todos os modelos, as funções de ligação logito e complemento log-log foram utilizadas para modelar a proporção de sobreviventes de longa duração (indivíduos não suscetíveis). Quanto à função de sobrevivência dos indivíduos suscetíveis, foram utilizados os modelos de Weibull e de Cox. Covariáveis foram consideradas tanto na proporção de sobreviventes de longa duração quanto na função de sobrevivência dos indivíduos suscetíveis. De modo geral, os modelos considerados se mostraram adequados para analisar os dados do ensaio clínico aleatorizado, indicando a cirurgia como a estratégia terapêutica mais eficiente. Indicaram também, que as covariáveis idade, hipertensão e diabetes mellitus exercem influência na ocorrência do óbito cardíaco, mas não no tempo até a ocorrência deste óbito nos pacientes suscetíveis.
Several models have been proposed in the literature with the aim of analyzing survival data when the population under study is assumed to be a mixture of susceptible (at risk) and not susceptible individuals to a specific event of interest. Such models are usually called long-term survivors models or cure rate models. In this work, several of these models (under both parametric and semi-parametric approaches) were considered to analyze the data from a randomized clinical trial conducted in order to compare three therapeutic strategies (surgery, angioplasty and medicine) used in the treatment of patients with multivessel coronary artery disease. For all models the logit and complementary log-log link functions were used to model the proportion of long-term survivors (not susceptible individuals). In regards to the survival function of the susceptible individuals, the Weibull and Cox models were used. Covariates were considered both in the proportion of longterm survivors and in the survival function of the susceptible individuals. Overall, the models considered were suitable for analyzing the data from the randomized clinical trial indicating surgery as the most effective therapeutic strategy. They also indicated that the covariates age, hypertension and diabetes mellitus exhibit influence on the occurrence of cardiac death, but not on the time to the occurrence of this death in susceptible patients.

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Alshanbari, Huda Mohammed H. "Additive Cox proportional hazards models for next-generation sequencing data." Thesis, University of Leeds, 2017. http://etheses.whiterose.ac.uk/19739/.

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Eighty-Nine Non-Small Cell Lung Cancer (NSCLC) patients experience chromosomal rearrangements called Copy Number Alteration (CNA), where the cells have abnormal number of copies in one or more regions in their genome, this genetic alteration are known to drive cancer development. An important aim of this thesis is to propose a way to combine the clinical covariate as fixed predictors with CNAs genomics windows as smoothing terms using the penalized additive Cox Proportional Hazards (PH) model. Most of the proposed prediction methods assume linearity of the CNAs genomic windows along with the clinical covariates. However, the continuous covariates can affect the hazard via more complicated nonlinear functional forms. Therefore, Cox PH model with continuous covariate are likely misspecified, because it is not fitting the correct functional form for the continuous covariates. Some reports of the work on combining the clinical covariates with high-dimensional genomic data in a clinical genomic prediction are based on standard Cox PH model. Most of them focus on applying variable selection to high-dimensional CNA genomic data. Our main interest is to propose a variable selection procedure to select important nonlinear effects from CNAs genomic-windows. Two different approaches of feature selection are presented which are discrete and shrinkage. Discrete feature selection is based on penalized univariate variable selection, which identify the subset of the CNAs genomic-windows have the strongest effects on the survival time, while feature selection by shrinkage works by adding a second penalty to the penalized partial log-likelihood, that leads to penalizing the smoothing coefficients in the model, as a result some of the smoothing coefficient are being set to the zero. For the NSCLC dataset, we find that the size of the tumor cells and spread cancer into the lymph nodes are significant factors that increase the hazard of the patients survival, and the estimate of the smooth log hazard ratio curves identify that some of the significant CNA genomic-windows contribute a higher or lower hazard of death to the survival of some significant CNA genomic-windows across the genome.

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Milner, A. D. "Detecting changes in covariate effect in the Cox proportional hazards model." Thesis, University of Newcastle Upon Tyne, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.239639.

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Pal, Subhamoy. "An Approach to Improving Test Powers in Cox Proportional Hazards Models." Bowling Green State University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1626893233789827.

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Andersson, Niklas. "Estimating Companies’ Survival in Financial Crisis : Using the Cox Proportional Hazards Model." Thesis, Uppsala universitet, Statistiska institutionen, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-225982.

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This master thesis is aimed towards answering the question What is the contribution from a company’s sector with regards to its survival of a financial crisis? with the sub question Can we use survival analysis on financial data to answer this?. Thus survival analysis is used to answer our main question which is seldom used on financial data. This is interesting since it will study how well survival analysis can be used on financial data at the same time as it will evaluate if all companies experiences a financial crisis in the same way. The dataset consists of all companies traded on the Swedish stock market during 2008. The results show that the survival method is very suitable the data that is used. The sector a company operated in has a significant effect. However the power is to low too give any indication of specific differences between the different sectors. Further on it is found that the group of smallest companies had much better survival than larger companies.

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Thompson, Kristina. "An Introduction to the Cox Proportional Hazards Model and Its Applications to Survival Analysis." Thesis, Southern Illinois University at Edwardsville, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=1571931.

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Statistical modeling of lifetime data, or survival analysis, is studied in many fields, including medicine, information technology and economics. This type of data gives the time to a certain event, such as death in studies of cancer treatment, or time until a computer program crashes. Researchers are often interested in how covariates affect the time to event and wish to determine ways of incorporating such covariates into statistical models. Covariates are explanatory variables that are suspected to affect the lifetime of interest. Lifetime data are typically subject to censoring and this fact needs to be taken into account when choosing the statistical model.

D.R. Cox (1972) proposed a statistical model that can be used to explore the relationship between survival and various covariates and takes censoring into account. This is called the Cox proportional hazards (PH) model. In particular, the model will be presented and estimation procedures for parameters and functions of interest will be developed. Statistical properties of the resulting estimators will be derived and used in developing inference procedures.

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Ansin, Elin. "An evaluation of the Cox-Snell residuals." Thesis, Uppsala universitet, Statistiska institutionen, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-256665.

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It is common practice to use Cox-Snell residuals to check for overall goodness of tin survival models. We evaluate the presumed relation of unit exponentially dis-tributed residuals for a good model t and evaluate under some violations of themodel. This is done graphically with the usual graphs of Cox-Snell residual andformally using Kolmogorov-Smirnov goodness of t test. It is observed that residu-als from a correctly tted model follow unit exponential distribution. However, theCox-Snell residuals do not seem to be sensitive to the violations of the model.

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Mlotshwa, Vintia Philile. "Modelling hepatotoxicity in HIV/TB co-infected patients: extensions of the Cox Proportional Hazards Model." Master's thesis, Faculty of Science, 2020. http://hdl.handle.net/11427/32806.

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Hepatotoxicity which is also known as liver damage is mainly caused by intake of medicine. It is common among patients who are co-administering Tuberculosis (TB) treatment and the antiretroviral therapy (ART) for the Human Immunodeficiency Viruses (HIV). If severe, hepatotoxicity sometimes necessitates cessation or interruption of treatment. Therefore, understanding, monitoring and managing hepatotoxicity in patients co-infected with TB and HIV is crucial for optimal treatment outcomes. Hepatotoxicity has been investigated in patients coinfected with TB and HIV, however, most studies have analyzed only the first occurrence of hepatotoxicity and discarded information relating to the resolution and recurrence of hepatotoxicity. Data from the ‘Starting Antiretroviral therapy at three Points in Tuberculosis' (SAPiT) trial is used in this project. This was a trial that was instrumental in finalizing treatment guidelines for patients co-infected with HIV and TB in South Africa. The clinical objectives of this project are to estimate incidence rates and determine risk factors associated with hepatotoxicity. The statistical objectives are to fit a Cox regression model, the resolution model of hepatotoxicity, and the extended Cox models for recurring events, including the Andersen Gill (AG) model, the Shared frailty model, the Prentice, Williams and Peterson (PWP) total time (TT) model, the PWP gap time (GT) model, as well as a Cox based recurrent model, that models only the second occurrence of hepatotoxicity. There were 593 patients assessed for hepatotoxicity in the study, 30% (179/593) developed the first occurrence of hepatotoxicity (grade >=1) and 2% (13/593) developed severe hepatotoxicity (grade >=3). Resolved cases (grade = 0) are 76% (136/179) and recurring cases (grade >=1) 24% (32/136). In the Cox multivariable analyses: time-varying treatment arm, older patients, alcohol consumption, low baseline total bilirubin and a positive baseline Hepatitis B surface antigen status, were associated with a higher risk of developing the first occurrence of hepatotoxicity. The extended Cox models (AG model, Shared frailty model, PWP TT model and PWP GT model) in combination identified that: time-varying treatment arm, older patients, alcohol consumption, baseline CD4 count that is greater than 50 cells per mm3 , low baseline total bilirubin, and a positive baseline Hepatitis B surface antigen status were associated with an increased risk of developing recurrent hepatotoxicity. In the resolution model multivariable analyses; non-consumers of alcohol and an abnormal liver function tests at baseline, were associated with an increased chance of resolving the first occurrence of hepatotoxicity. In the multivariable analyses of the recurrent model: younger patients and the time-varying treatment arm were associated with the development of the second occurrence hepatotoxicity. Since the Cox regression model utilized data up to the first occurrence of hepatotoxicity, in some instances, the time-varying treatment effect based on the Cox regression model was closer to unity and marginally significant. And the corresponding effect based on the recurrent event models (AG model, Shared frailty model, PWP TT model, PWP GT model and the recurrent model), that utilized data of the first and second occurrence of hepatotoxicity, generally produced a time-varying treatment effect slightly far from unity with a strong statistical significance. This trend was similar for other predictors of hepatotoxicity, like CD4 count and alcohol consumption. In conclusion, hepatotoxicity is common in this study, however, it is often transient or mild and did not necessitate treatment interruption. However, close monitoring of patients especially in the first 5 months of TB-treatment is recommended. The PWP TT model seemed to be the best model for modelling recurring hepatotoxicity, since the identified risk factors that were associated with hepatotoxicity, changed from the first occurrence of hepatotoxicity to the second occurrence of hepatotoxicity.

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Hoglin, Phillip J. "Survival analysis and accession optimization of prior enlisted United States Marine Corps officers." Thesis, Monterey, California. Naval Postgraduate School, 2004. http://hdl.handle.net/10945/1673.

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Approved for public release, distribution is unlimited
The purpose of this thesis is to firstly analyze the determinants on the survival of United States Marine Corps Officers, and secondly, to develop the methodology to optimize the accessions of prior and non-prior enlisted officers. Using data from the Marine Corps Officer Accession Career file (MCCOAC), the Cox Proportional Hazards Model is used to estimate the effects of officer characteristics on their survival as a commissioned officer in the USMC. A Markov model for career transition is combined with fiscal data to determine the optimum number of prior and non-prior enlisted officers under the constraints of force structure and budget. The findings indicate that prior enlisted officers have a better survival rate than their non-prior enlisted counterparts. Additionally, officers who are married, commissioned through MECEP, graduate in the top third of their TBS class, and are assigned to a combat support MOS have a better survival rate than officers who are unmarried, commissioned through USNA, graduate in the middle third of their TBS class, and are assigned to either combat or combat service support MOS. The findings also indicate that the optimum number of prior enlisted officer accessions may be considerably lower than recent trends and may differ across MOS. Based on the findings; it is recommended that prior enlisted officer accession figures be reviewed.
Major, Australian Army

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Bertke, Stephen J. "A Simulation Study of the Cox Proportional Hazards Model and the Nested Case-Control Study Design." University of Cincinnati / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1307321495.

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Frejd, Ellen, and Jenny Sjödin. "Är kognitiva test relaterade till demens? : En utvidgning av Cox Proportional Hazards Model med tidsvarierande kovariat." Thesis, Umeå universitet, Statistik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-184984.

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Syftet med uppsatsen är att undersöka samband mellan kognitiva test och insjuknande i demens. Demens är en samlingsdiagnos för sjukdomar som är kopplade till nedsatt kognitiv förmåga. Symptom inkluderar försämrad minnesfunktion och personlighetsförändringar. Data är insamlad av den longitudinella studien Betula vid Umeå universitet. Vid upprepade testtillfällen genomfördes minnestester för att mäta deltagarnas minnesfunktion och kognitiva förmåga. Minnestestresultat analyseras med en utvidgning av Cox Proportional Hazards Model med tidsvarierande kovariat. Genom att analysera testresultat som varierar över tid erhölls uppdaterad information om deltagarnas kognitiva tillstånd. Vidare jämförs den utvidgade modellen med en klassisk Coxmodell med baslinjedata. Modellering inkluderar även kontrollvariabeln ApoE4 som är en genvariant som innebär förhöjd demensrisk. Resultaten visar samband mellan demens och test som mäter episodminne och visuospatial förmåga. Den utvidgade modellen med tidsvarierande testresultat föredras som modellval.

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Herich, Lena [Verfasser], and Karl [Akademischer Betreuer] Wegscheider. "Erweiterung des Cox-Proportional-Hazards-Modells um latente Faktoren und latente Klassen / Lena Herich. Betreuer: Karl Wegscheider." Hamburg : Staats- und Universitätsbibliothek Hamburg, 2012. http://d-nb.info/1030365326/34.

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Herich, Lena Verfasser], and Karl [Akademischer Betreuer] [Wegscheider. "Erweiterung des Cox-Proportional-Hazards-Modells um latente Faktoren und latente Klassen / Lena Herich. Betreuer: Karl Wegscheider." Hamburg : Staats- und Universitätsbibliothek Hamburg, 2012. http://nbn-resolving.de/urn:nbn:de:gbv:18-59079.

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He, Bin. "APPLICATION OF THE EMPIRICAL LIKELIHOOD METHOD IN PROPORTIONAL HAZARDS MODEL." Doctoral diss., University of Central Florida, 2006. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/4384.

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In survival analysis, proportional hazards model is the most commonly used and the Cox model is the most popular. These models are developed to facilitate statistical analysis frequently encountered in medical research or reliability studies. In analyzing real data sets, checking the validity of the model assumptions is a key component. However, the presence of complicated types of censoring such as double censoring and partly interval-censoring in survival data makes model assessment difficult, and the existing tests for goodness-of-fit do not have direct extension to these complicated types of censored data. In this work, we use empirical likelihood (Owen, 1988) approach to construct goodness-of-fit test and provide estimates for the Cox model with various types of censored data. Specifically, the problems under consideration are the two-sample Cox model and stratified Cox model with right censored data, doubly censored data and partly interval-censored data. Related computational issues are discussed, and some simulation results are presented. The procedures developed in the work are applied to several real data sets with some discussion.
Ph.D.
Department of Mathematics
Sciences
Mathematics

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Söderberg, Daniel. "Model estimation of the longevity for cars registered in Sweden using survival analysis and Cox proportional hazards model." Thesis, Uppsala universitet, Statistiska institutionen, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-227520.

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Time-to-event data is used in this thesis to analyze private cars’ longevity in Sweden. Thedataset is provided by Trafikanalys and contains all registered, deregistered or temporary deregisteredcars in Sweden during the time period 2000 - 2012.A Cox proportional hazards model is fitted, including variables such as car manufacturer andcar body. The results show that directly imported cars have a much shorter median survivalcompared to non-imported cars. The convertible cars have the longest median survival amongthe five different car bodies. Sedan and station wagon body types have the shortest mediansurvival. Volvo and Mercedes have the longest survival while Renault, Ford and Opel have theshortest survival. The model fits the data reasonably well, and the assumption of proportionalhazards holds for most of the variables.

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Ekman, Anna. "Variable selection for the Cox proportional hazards model : A simulation study comparing the stepwise, lasso and bootstrap approach." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-130521.

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In a regression setting with a number of measured covariates not all may be relevant to the response. By reducing the numbers of covariates included in the final model we could improve its prediction accurarcy as well as making it easier to interpret. In survival analysis, the study of time-to-event data, the most common form of regression is the semi-parametric Cox proportional hazard (PH) model. In this thesis we have compared three different ways to perform variable selection in the Cox PH model, stepwise regression, lasso and bootstrap. By simulating survival data we could control which covariates that were significant for the response. Fitting the Cox PH model to these data using the three different variable selection methods we could evaluate how well each method performs in finding the correct model. We found that while bootstrap in some cases could improve the stepwise approach its performance is strongly effected by the choice of inclusion frequency. Lasso performed equivalent or slightly better than the stepwise method for data with weak effects. However, when the data instead consists of strong effects, the performance of stepwise is considerably better than the performance of lasso.
Vid regression söks sambandet mellan en beroende variabel och en eller flera förklarande variabler. Även om vi har tillgång till många förklarande variabler är det dock inte säkert att alla påverkar den beroende variabeln. Genom att minska antalet variabler som inkluderas i den slutgiltiga modellen kan man förbättra dess prediktionsförmåga samtidigt som den blir lättare att tolka. Inom överlevnadslys är en av de vanligaste regressionsmetoderna den semi-parametriska Cox proportional hazard (PH) model. I den här uppsatsen har vi jämfört tre olika metoder för variabel selektion i Cox PH model, stegvis regression, lasso och bootstrap. Genom att simulera överlevnadsdata kan vi styra vilka variabler som påverkar den beroende variabelen. Det blir då möjligt att utvärdera hur väl de olika metoderna lyckas med att inkludera dessa variabler i den slutgiltiga Cox PH model. Vi fann att bootstrap i vissa situationer gav bättre resultat än den stegvisa regressionen, dock varierar resultatet väldigt mycket beroende på valet av inklusionsfrekvens. Resultaten av lasso och stegvis regression är likvärdiga, eller till fördel för lasso, så länge datat innehåller svagare effekter. När datat istället består av starkare effekter ger dock den stegvisa regressionen mycket bättre resultat än lasso.

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McCosker, Helen Clare. "Prognostic significance of IGF and ECM induced signalling proteins in breast cancer patients." Thesis, Queensland University of Technology, 2012. https://eprints.qut.edu.au/53580/1/Helen_McCosker_Thesis.pdf.

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Breast cancer is a leading contributor to the burden of disease in Australia. Fortunately, the recent introduction of diverse therapeutic strategies have improved the survival outcome for many women. Despite this, the clinical management of breast cancer remains problematic as not all approaches are sufficiently sophisticated to take into account the heterogeneity of this disease and are unable to predict disease progression, in particular, metastasis. As such, women with good prognostic outcomes are exposed to the side effects of therapies without added benefit. Furthermore, women with aggressive disease for whom these advanced treatments would deliver benefit cannot be distinguished and opportunities for more intensive or novel treatment are lost. This study is designed to identify novel factors associated with disease progression, and the potential to inform disease prognosis. Frequently overlooked, yet common mediators of disease are the interactions that take place between the insulin-like growth factor (IGF) system and the extracellular matrix (ECM). Our laboratory has previously demonstrated that multiprotein insulin-like growth factor-I (IGF-I): insulin-like growth factor binding protein (IGFBP): vitronectin (VN) complexes stimulate migration of breast cancer cells in vitro, via the cooperative involvement of the insulin-like growth factor type I receptor (IGF-IR) and VN-binding integrins. However, the effects of IGF and ECM protein interactions on the dissemination and progression of breast cancer in vivo are unknown. It was hypothesised that interactions between proteins required for IGF induced signalling events and those within the ECM contribute to breast cancer metastasis and are prognostic and predictive indicators of patient outcome. To address this hypothesis, semiquantitative immunohistochemistry (IHC) analyses were performed to compare the extracellular and subcellular distribution of IGF and ECM induced signalling proteins between matched normal, primary cancer, and metastatic cancer among archival formalin-fixed paraffin-embedded (FFPE) breast tissue samples collected from women attending the Princess Alexandra Hospital, Brisbane. Multivariate Cox proportional hazards (PH) regression survival models in conjunction with a modified „purposeful selection of covariates. method were applied to determine the prognostic potential of these proteins. This study provides the first in-depth, compartmentalised analysis of the distribution of IGF and ECM induced signalling proteins. As protein function and protein localisation are closely correlated, these findings provide novel insights into IGF signalling and ECM protein function during breast cancer development and progression. Distinct IGF signalling and ECM protein immunoreactivity was observed in the stroma and/or in subcellular locations in normal breast, primary cancer and metastatic cancer tissues. Analysis of the presence and location of stratifin (SFN) suggested a causal relationship in ECM remodelling events during breast cancer development and progression. The results of this study have also suggested that fibronectin (FN) and ¥â1 integrin are important for the formation of invadopodia and epithelial-to-mesenchymal transition (EMT) events. Our data also highlighted the importance of the temporal and spatial distribution of IGF induced signalling proteins in breast cancer metastasis; in particular, SFN, enhancer-of-split and hairy-related protein 2 (SHARP-2), total-akt/protein kinase B 1 (Total-AKT1), phosphorylated-akt/protein kinase B (P-AKT), extracellular signal-related kinase-1 and extracellular signal-related kinase-2 (ERK1/2) and phosphorylated-extracellular signal-related kinase-1 and extracellular signal-related kinase-2 (P-ERK1/2). Multivariate survival models were created from the immunohistochemical data. These models were found to fit well with these data with very high statistical confidence. Numerous prognostic confounding effects and effect modifications were identified among elements of the ECM and IGF signalling cascade and corroborate the survival models. This finding provides further evidence for the prognostic potential of IGF and ECM induced signalling proteins. In addition, the adjusted measures of associations obtained in this study have strengthened the validity and utility of the resulting models. The findings from this study provide insights into the biological interactions that occur during the development of breast tissue and contribute to disease progression. Importantly, these multivariate survival models could provide important prognostic and predictive indicators that assist the clinical management of breast disease, namely in the early identification of cancers with a propensity to metastasise, and/or recur following adjuvant therapy. The outcomes of this study further inform the development of new therapeutics to aid patient recovery. The findings from this study have widespread clinical application in the diagnosis of disease and prognosis of disease progression, and inform the most appropriate clinical management of individuals with breast cancer.

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Cai, Jianwen. "Generalized estimating equations for censored multivariate failure time data /." Thesis, Connect to this title online; UW restricted, 1992. http://hdl.handle.net/1773/9581.

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Youssef, Ibrahim Mohamed. "Multi-Platform Molecular Data Integration and Disease Outcome Analysis." Diss., Virginia Tech, 2016. http://hdl.handle.net/10919/73580.

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One of the most common measures of clinical outcomes is the survival time. Accurately linking cancer molecular profiling with survival outcome advances clinical management of cancer. However, existing survival analysis relies intensively on statistical evidence from a single level of data, without paying much attention to the integration of interacting multi-level data and the underlying biology. Advances in genomic techniques provide unprecedented power of characterizing the cancer tissue in a more complete manner than before, opening the opportunity of designing biologically informed and integrative approaches for survival analysis. Many cancer tissues have been profiled for gene expression levels and genomic variants (such as copy number alterations, sequence mutations, DNA methylation, and histone modification). However, it is not clear how to integrate the gene expression and genetic variants to achieve a better prediction and understanding of the cancer survival. To address this challenge, we propose two approaches for data integration in order to both biologically and statistically boost the features selection process for proper detection of the true predictive players of survival. The first approach is data-driven yet biologically informed. Consistent with the biological hierarchy from DNA to RNA, we prioritize each survival-relevant feature with two separate scores, predictive and mechanistic. With mRNA expression levels in concern, predictive features are those mRNAs whose variation in expression levels are associated with the survival outcome, and mechanistic features are those mRNAs whose variation in expression levels are associated with genomic variants (copy number alterations (CNAs) in this study). Further, we propose simultaneously integrating information from both the predictive model and the mechanistic model through our new approach GEMPS (Gene Expression as a Mediator for Predicting Survival). Applied on two cancer types (ovarian and glioblastoma multiforme), our method achieved better prediction power than peer methods. Gene set enrichment analysis confirms that the genes utilized for the final survival analysis are biologically important and relevant. The second approach is a generic mathematical framework to biologically regularize the Cox's proportional hazards model that is widely used in survival analysis. We propose a penalty function that both links the mechanistic model to the clinical model and reflects the biological downstream regulatory effect of the genomic variants on the mRNA expression levels of the target genes. Fast and efficient optimization principles like the coordinate descent and majorization-minimization are adopted in the inference process of the coefficients of the Cox model predictors. Through this model, we develop the regulator-target gene relationship to a new one: regulator-target-outcome relationship of a disease. Assessed via a simulation study and analysis of two real cancer data sets, the proposed method showed better performance in terms of selecting the true predictors and achieving better survival prediction. The proposed method gives insightful and meaningful interpretability to the selected model due to the biological linking of the mechanistic model and the clinical model. Other important forms of clinical outcomes are monitoring angiogenesis (formation of new blood vessels necessary for tumor to nourish itself and sustain its existence) and assessing therapeutic response. This can be done through dynamic imaging, in which a series of images at different time instances are acquired for a specific tumor site after injection of a contrast agent. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a noninvasive tool to examine tumor vasculature patterns based on accumulation and washout of the contrast agent. DCE-MRI gives indication about tumor vasculature permeability, which in turn indicates the tumor angiogenic activity. Observing this activity over time can reflect the tumor drug responsiveness and efficacy of the treatment plan. However, due to the limited resolution of the imaging scanners, a partial-volume effect (PVE) problem occurs, which is the result of signals from two or more tissues combining together to produce a single image concentration value within a pixel, with the effect of inaccurate estimation to the values of the pharmacokinetic parameters. A multi-tissue compartmental modeling (CM) technique supported by convex analysis of mixtures is used to mitigate the PVE by clustering pixels and constructing a simplex whose vertices are of a single compartment type. CAM uses the identified pure-volume pixels to estimate the kinetics of the tissues under investigation. We propose an enhanced version of CAM-CM to identify pure-volume pixels more accurately. This includes the consideration of the neighborhood effect on each pixel and the use of a barycentric coordinate system to identify more pure-volume pixels and to test those identified by CAM-CM. Tested on simulated DCE-MRI data, the enhanced CAM-CM achieved better performance in terms of accuracy and reproducibility.
Ph. D.

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Carstens, Wiehahn Alwyn. "Regression analysis of caterpillar 793D haul truck engine failure data and through-life diagnostic information using the proportional hazards model." Thesis, Stellenbosch : Stellenbosch University, 2012. http://hdl.handle.net/10019.1/20333.

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Thesis (MScEng)--Stellenbosch University, 2012.
ENGLISH ABSTRACT: Physical Asset Management (PAM) is becoming a greater concern for companies in industry today. The widely accepted British Standards Institutes’ specification for optimized management of physical assets and infrastructure is PAS55. According to PAS55, PAM is the “systematic and co-ordinated activities and practices through which an organization optimally manages its physical assets, and their associated performance, risks and expenditures over their life cycle for the purpose of achieving its organizational strategic plan”. One key performance area of PAM is Asset Care Plans (ACP). These plans are maintenance strategies which improve or ensure acceptable asset reliability and performance during its useful life. Maintenance strategies such as Condition Based Maintenance (CBM) acts upon Condition Monitoring (CM) data, disregarding the previous failure histories of an asset. Other maintenance strategies, such as Usage Based Maintenance (UBM), is based on previous failure histories, and does not consider CM data. Regression models make use of both CM data and previous failure histories to develop a model which represents the underlying failure behaviour of the asset under study. These models can be of high value in ACP development due to the fact that Residual Useful Life (RUL) can be estimated and/or the long term life cycle cost can be optimized. The objective of this thesis was to model historical failure data and CM data well enough so that RUL or optimized preventive maintenance instant estimations can be made. These estimates were used in decision models to develop maintenance schedules, i.e. ACPs. Several regression models were evaluated to determine the most suitable model to achieve the objectives of this thesis. The model found to be most suitable for this research project was the Proportional Hazards Model (PHM). A comprehensive investigation on the PHM was undertaken focussing on the mathematics and the practical implementation thereof. Data obtained from the South African mining industry was modelled with the Weibull PHM. It was found that the developed model produced estimates which were accurate representations of reality. These findings provide an exciting basis for the development of futureWeibull PHMs that could result in huge maintenance cost savings and reduced failure occurrences.
AFRIKAANSE OPSOMMING: Fisiese Bate Bestuur (FBB) is besig om ’n groter bekommernis vir maatskappye in die bedryf te word. Die Britse Standaarde Instituut se spesifikasie vir optimale bestuur van fisiese bates en infrastruktuur is PAS55. Volgens PAS55 is FBB die “sistematiese en gekoördineerde aktiwiteite en praktyke wat deur ’n organisasie optimaal sy fisiese bates, hul verwante prestasie, risiko’s en uitgawes vir die doel van die bereiking van sy organisatoriese strategiese plan beheer oor hul volle lewensiklus te bestuur”. Een Sleutel Fokus Area (SFA) van FBB is Bate Versorgings Plan (BVP) ontwikkeling. Hierdie is onderhouds strategieë wat bate betroubaarheid verbeter of verseker tydens die volle bruikbare lewe van die bate. Een onderhoud strategie is Toestands Gebasseeerde Onderhoud (TGO) wat besluite baseer op Toestand Monitering (TM) informasie maar neem nie die vorige falingsgeskiedenis van die bate in ag nie. Ander onderhoud strategieë soos Gebruik Gebasseerde Onderhoud (GGO) is gebaseer op historiese falingsdata maar neem nie TM inligting in ag nie. Regressiemodelle neem beide TM data en historiese falings geskiedenis data in ag ten einde die onderliggende falings gedrag van die gegewe bate te verteenwoordig. Hierdie modelle kan baie nuttig wees vir BVP ontwikkeling te danke aan die feit dat Bruikbare Oorblywende Lewe (BOL) geskat kan word en/of die langtermyn lewenssilus koste geoptimeer kan word. Die doelwit van hierdie tesis was om historiese falingsdata en TT data goed genoeg te modelleer sodat BOL of optimale langtermyn lewensiklus kostes bepaal kan word om opgeneem te word in BVP ontwikkeling. Hierdie bepalings word dan gebruik in besluitnemings modelle wat gebruik kan word om onderhoud skedules op te stel, d.w.s. om ’n BVP te ontwikkel. Verskeie regressiemodelle was geëvalueer om die regte model te vind waarmee die doel van hierdie tesis te bereik kan word. Die mees geskikte model vir die navorsingsprojek was die Proporsionele Gevaarkoers Model (PGM). ’n Omvattende ondersoek oor die PGM is onderneem wat fokus op die wiskunde en die praktiese implementering daarvan. Data is van die Suid-Afrikaanse mynbedryf verkry en is gemodelleer met behulp van die Weibull PGM. Dit was bevind dat die ontwikkelde model resultate geproduseer het wat ’n akkurate verteenwoordinging van realiteit is. Hierdie bevindinge bied ’n opwindende basis vir die ontwikkeling van toekomstige Weibull Proporsionele Gevaarkoers Modelle wat kan lei tot groot onderhoudskoste besparings en minder onverwagte falings.

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Li, Qiuju. "Statistical inference for joint modelling of longitudinal and survival data." Thesis, University of Manchester, 2014. https://www.research.manchester.ac.uk/portal/en/theses/statistical-inference-for-joint-modelling-of-longitudinal-and-survival-data(65e644f3-d26f-47c0-bbe1-a51d01ddc1b9).html.

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In longitudinal studies, data collected within a subject or cluster are somewhat correlated by their very nature and special cares are needed to account for such correlation in the analysis of data. Under the framework of longitudinal studies, three topics are being discussed in this thesis. In chapter 2, the joint modelling of multivariate longitudinal process consisting of different types of outcomes are discussed. In the large cohort study of UK north Stafforshire osteoarthritis project, longitudinal trivariate outcomes of continuous, binary and ordinary data are observed at baseline, year 3 and year 6. Instead of analysing each process separately, joint modelling is proposed for the trivariate outcomes to account for the inherent association by introducing random effects and the covariance matrix G. The influence of covariance matrix G on statistical inference of fixed-effects parameters has been investigated within the Bayesian framework. The study shows that by joint modelling the multivariate longitudinal process, it can reduce the bias and provide with more reliable results than it does by modelling each process separately. Together with the longitudinal measurements taken intermittently, a counting process of events in time is often being observed as well during a longitudinal study. It is of interest to investigate the relationship between time to event and longitudinal process, on the other hand, measurements taken for the longitudinal process may be potentially truncated by the terminated events, such as death. Thus, it may be crucial to jointly model the survival and longitudinal data. It is popular to propose linear mixed-effects models for the longitudinal process of continuous outcomes and Cox regression model for survival data to characterize the relationship between time to event and longitudinal process, and some standard assumptions have been made. In chapter 3, we try to investigate the influence on statistical inference for survival data when the assumption of mutual independence on random error of linear mixed-effects models of longitudinal process has been violated. And the study is conducted by utilising conditional score estimation approach, which provides with robust estimators and shares computational advantage. Generalised sufficient statistic of random effects is proposed to account for the correlation remaining among the random error, which is characterized by the data-driven method of modified Cholesky decomposition. The simulation study shows that, by doing so, it can provide with nearly unbiased estimation and efficient statistical inference as well. In chapter 4, it is trying to account for both the current and past information of longitudinal process into the survival models of joint modelling. In the last 15 to 20 years, it has been popular or even standard to assume that longitudinal process affects the counting process of events in time only through the current value, which, however, is not necessary to be true all the time, as recognised by the investigators in more recent studies. An integral over the trajectory of longitudinal process, along with a weighted curve, is proposed to account for both the current and past information to improve inference and reduce the under estimation of effects of longitudinal process on the risk hazards. A plausible approach of statistical inference for the proposed models has been proposed in the chapter, along with real data analysis and simulation study.

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Brännmark, My, and Ellen Fors. "Modellering av åtgärdsintervall för vägar med tung trafik." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-160057.

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In Sweden, there has been an long term effort to allow as heavy traffic as possible, provided thatthe road network can handle it. This is because heavy traffic offers a competitive advantage withsocio-economic gains. In July 2018, the Swedish Transport Administration made 12 percent ofthe Swedish road network avaliable for the new maximum vehicle weight of 74 tonnes, basedon a legislative change from 2017. It is known that heavy traffic has a negative effect on thedegradation of the road, but it prevails divided opinions on whether 74 tonnes have a greaterimpact on the degradation rate compared to previous maximum gross weights of 64 tonnes.The 74 tonne vehicles have the same allowed axle load, which means more axles per vehicle. Some argue that an increased total load and more axles affect the degradation associated withtime-dependent material properties, while others argue that 74 tonnes mean fewer heavy vehiclesoverall, and thus should have a positive impact on the road’s lifespan. The construction companySkanska therefore requests a statistical analysis that enables to nuance the effects that heavytraffic has on the Swedish state road network. Since there is very limited data on the effect of 74 tonne traffic, this Master thesis instead focuseson modeling heavy traffic in general in order to be able to draw conclusions on which variablesare significant for a road’s lifetime. The method used is survival analysis where the lifetimeof the road is defined as the time between two maintenance treatments. The model selectedis the semi-parametric ’Cox Proportional Hazard Model’. The model is fitted with data froman open source database called LTPP (Long Term Pavement Performance) which is providedby the National Road and Transport Research Institute (VTI). The result of the modeling ispresented with hazard ratios, which is the relative risk that a road will require maintance atthe next time stamp compared to a reference category. The covariates that turned out to besignificant for a road’s lifetime and thus are included in the model are; lane width, undergroundtype, speed limit, asphalt layer thickness, bearing layer thickness and proportion of heavy traffic. Survival curves estimated by the model are also presented. In addition, a sensitivity analysis ismade by exploring survival curves estimated for different scenarios, with different combinationsof covariate levels.The results is then compared with previous studies on the subject. The most interesting finding isa case study from Finland since Finland allow 76 tonne vehicles since 2013. In the comparison,the model’s significant variables are confirmed, but the significance of precipitation and thenumber of axes for a roads lifetime is also highlighted

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Hallström, Richard. "Estimating Loss-Given-Default through Survival Analysis : A quantitative study of Nordea's default portfolio consisting of corporate customers." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-122914.

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In Sweden, all banks must report their regulatory capital in their reports to the market and their models for calculating this capital must be approved by the financial authority, Finansinspektionen. The regulatory capital is the capital that a bank has to hold as a security for credit risk and this capital should serve as a buffer if they would loose unexpected amounts of money in their lending business. Loss-Given-Default (LGD) is one of the main drivers of the regulatory capital and the minimum required capital is highly sensitive to the reported LGD. Workout LGD is based on the discounted future cash flows obtained from defaulted customers. The main issue with workout LGD is the incomplete workouts, which in turn results in two problems for banks when they calculate their workout LGD. A bank either has to wait for the workout period to end, in which some cases take several years, or to exclude or make rough assumptions about those incomplete workouts in their calculations. In this study the idea from Survival analysis (SA) methods has been used to solve these problems. The mostly used SA model, the Cox proportional hazards model (Cox model), has been applied to investigate the effect of covariates on the length of survival for a monetary unit. The considered covariates are Country of booking, Secured/Unsecured, Collateral code, Loan-To-Value, Industry code, Exposure-At- Default and Multi-collateral. The data sample was first split into 80 % training sample and 20 % test sample. The applied Cox model was based on the training sample and then validated with the test sample through interpretation of the Kaplan-Meier survival curves for risk groups created from the prognostic index (PI). The results show that the model correctly rank the expected LGD for new customers but is not always able to distinguish the difference between risk groups. With the results presented in the study, Nordea can get an expected LGD for newly defaulted customers, given the customers’ information on the considered covariates in this study. They can also get a clear picture of what factors that drive a low respectively high LGD.
I Sverige måste alla banker rapportera sitt lagstadgade kapital i deras rapporter till marknaden och modellerna för att beräkna detta kapital måste vara godkända av den finansiella myndigheten, Finansinspektionen. Det lagstadgade kapitalet är det kapital som en bank måste hålla som en säkerhet för kreditrisk och den agerar som en buffert om banken skulle förlora oväntade summor pengar i deras utlåningsverksamhet. Loss- Given-Default (LGD) är en av de främsta faktorerna i det lagstadgade kapitalet och kravet på det minimala kapitalet är mycket känsligt för det rapporterade LGD. Workout LGD är baserat på diskonteringen av framtida kassaflöden från kunder som gått i default. Det huvudsakliga problemet med workout LGD är ofullständiga workouts, vilket i sin tur resulterar i två problem för banker när de ska beräkna workout LGD. Banken måste antingen vänta på att workout-perioden ska ta slut, vilket i vissa fall kan ta upp till flera år, eller så får banken exkludera eller göra grova antaganden om dessa ofullständiga workouts i sina beräkningar. I den här studien har idén från Survival analysis (SA) metoder använts för att lösa dessa problem. Den mest använda SA modellen, Cox proportional hazards model (Cox model), har applicerats för att undersöka effekten av kovariat på livslängden hos en monetär enhet. De undersökta kovariaten var Land, Säkrat/Osäkrat, Kollateral-kod, Loan-To-Value, Industri-kod Exposure-At-Default och Multipla-kollateral. Dataurvalet uppdelades först i 80 % träningsurval och 20 % testurval. Den applicerade Cox modellen baserades på träningsurvalet och validerades på testurvalet genom tolkning av Kaplan-Meier överlevnadskurvor för riskgrupperna skapade från prognosindexet (PI). Med de presenterade resultaten kan Nordea beräkna ett förväntat LGD för nya kunder i default, givet informationen i den här studiens undersökta kovariat. Nordea kan också få en klar bild över vilka faktorer som driver ett lågt respektive högt LGD.

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Latorre, Maria do Rosario Dias de Oliveira. "Comparação entre alguns métodos estatísticos em análise de sobrevivência: aplicação em uma coorte de pacientes com câncer de pênis." Universidade de São Paulo, 1996. http://www.teses.usp.br/teses/disponiveis/6/6132/tde-12112014-153823/.

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O objetivo deste trabalho foi comparar o desempenho do modelo de riscos proporcionais de Cox convencional, modelo de Cox modificado quando os riscos não são proporcionais e o modelo de análise de sobrevida baseado na teoria de processos de contagem. Para tanto utilizou-se uma coorte de 648 pacientes portadores de câncer de pênis, atendidos no Departamento de Cirurgia Pélvica do Hospital A. C. Camargo, no período de 1953 a 1985. Dessa coorte foram selecionadas três amostras com o objetivo de validar internamente os resultados da análise de sobrevida do banco de dados original. Os resultados do modelo de riscos proporcionais de Cox, no banco de dados original, foram confirmados por uma das amostras desse conjunto de dados. Apenas o estadiamento N foi confirmado como fator prognóstico também nas outras duas amostras. O modelo de riscos proporcionais de Cox e o modelo de análise de sobrevida baseado na teoria de processos de contagem apresentaram resultados semelhantes, na definição dos fatores prognósticos dessa coorte de pacientes com câncer de pênis. O modelo utilizando processos de contagem é mais sofisticado, do ponto de vista matemático. Porém o modelo de Cox está disponível em grande número de pacotes estatísticos e a interpretação de seus coeficientes se faz com maior facilidade. Por isso, talvez, continue a ser a técnica estatística mais utilizada quando o objetivo do estudo é definir fatores prognósticos e grupos de risco. Os fatores prognósticos para a sobrevida de pacientes com câncer de pênis foram os estadiamentos T e N e o grau de diferenciação do tumor. Esses resultados foram ajustados pelo ano de início de tratamento no Hospital A.C. Camargo. Os pacientes com prognóstico favorável foram os que apresentaram tumor pequeno, sem presença de linfonodos clinicamente positivos, e tumor bem diferenciado.
The aim of this study was to compare the performance of the Cox proportional hazards model, the Cox model with time-dependent covariates and the survival model using the counting process theory. These methods were applied in a cohort of 648 patients with penile cancer treated at the Department of Pelvic Surgery, Hospital A.C. Camargo (São Paulo-Brazil), between 1953 and 1985. Three samples were selected from the total database in order to check the internal validity. The prognostic factors selected using the Cox proportional hazards model were the same in one sample. The only prognostic factor selected in all samples was the N stage. The T and N stages, and the grade of differentiation were independent prognostic factors of survival using both the Cox proportional hazards model and the survival,model using the counting process theory. The statistical significance was the same and even the values of estimation of the coefficients were very close. The survival model using the counting process is more sophisticated from the mathematical point of view, but the Cox model is more available in statistical software, and, probably because of this, is more applied in survival analysis than the model using the counting processo Patients with small tumors, clinically negatives nodes and well differentiated tumors showed a favorable prognosis. These results were adjusted by year of the beginning in the study.

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Alves, Karina Lumena de Freitas. "Análise de sobrevivência de bancos privados no Brasil." Universidade de São Paulo, 2009. http://www.teses.usp.br/teses/disponiveis/18/18140/tde-28102009-103529/.

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Diante da importância do sistema financeiro para a economia de um país, faz-se necessária sua constante fiscalização. Nesse sentido, a identificação de problemas existentes no cenário bancário apresenta-se fundamental, visto que as crises bancárias ocorridas mundialmente ao longo da história mostraram que a falta de credibilidade bancária e a instabilidade do sistema financeiro geram enormes custos financeiros e sociais. Os modelos de previsão de insolvência bancária são capazes de identificar a condição financeira de um banco devido ao valor correspondente da sua probabilidade de insolvência. Dessa forma, o presente trabalho teve como objetivo identificar os principais indicadores característicos da insolvência de bancos privados no Brasil. Para isso, foi utilizada a técnica de análise de sobrevivência em uma amostra de 70 bancos privados no Brasil, sendo 33 bancos insolventes e 37 bancos solventes. Foi possível identificar os principais indicadores financeiros que apresentaram-se significativos para explicar a insolvência de bancos privados no Brasil e analisar a relação existente entre estes indicador e esta probabilidade. O resultado deste trabalho permitiu a realização de importantes constatações para explicar o fenômeno da insolvência de bancos privados no Brasil, bem como, permitiu constatar alguns aspectos característicos de bancos em momentos anteriores à sua insolvência.
The financial system is very important to the economy of a country, than its supervision is necessary. Accordingly, the identification of problems in the banking scenario is fundamental, since the banking crisis occurring worldwide throughout history have shown that and instability of the financial system generates huge financial and social costs. The banking failure prediction models are able to identify the financial condition of a bank based on the value of its probability of insolvency. Thus, this study aimed to identify the main financial ratios that can explain the insolvency of private banks in Brazil. For this, it was used the survival analysis to analize a sample of 70 private banks in Brazil, with 33 solvent banks and 37 insolvent banks. It was possible to identify the key financial indicators that were significantly to explain the bankruptcy of private banks in Brazil and it was possible to examine the relationship between these financial ratios and the probability of bank failure. The result of this work has enabled the achievement of important findings to explain the phenomenon of the bankruptcy of private banks in Brazil, and has seen some characteristic of banks in times prior to its insolvency.

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Hein, Misty. "Occupational Cohort Studies and the Nested Case-Control Study Design." University of Cincinnati / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1250795434.

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Yu, Jianxiong. "Pavement Service Life Estimation And Condition Prediction." See Full Text at OhioLINK ETD Center (Requires Adobe Acrobat Reader for viewing), 2005. http://www.ohiolink.edu/etd/view.cgi?toledo1132896646.

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Dissertation (Ph.D.)--University of Toledo, 2005.
Typescript. "A dissertation [submitted] as partial fulfillment of the requirements of the Doctor of Philosophy degree in Engineering." Bibliography: leaves 69-74.

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Tran, Xuan Quang. "Les modèles de régression dynamique et leurs applications en analyse de survie et fiabilité." Thesis, Bordeaux, 2014. http://www.theses.fr/2014BORD0147/document.

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Cette thèse a été conçu pour explorer les modèles dynamiques de régression, d’évaluer les inférences statistiques pour l’analyse des données de survie et de fiabilité. Ces modèles de régression dynamiques que nous avons considérés, y compris le modèle des hasards proportionnels paramétriques et celui de la vie accélérée avec les variables qui peut-être dépendent du temps. Nous avons discuté des problèmes suivants dans cette thèse.Nous avons présenté tout d’abord une statistique de test du chi-deux généraliséeY2nquiest adaptative pour les données de survie et fiabilité en présence de trois cas, complètes,censurées à droite et censurées à droite avec les covariables. Nous avons présenté en détailla forme pratique deY2nstatistique en analyse des données de survie. Ensuite, nous avons considéré deux modèles paramétriques très flexibles, d’évaluer les significations statistiques pour ces modèles proposées en utilisantY2nstatistique. Ces modèles incluent du modèle de vie accélérés (AFT) et celui de hasards proportionnels (PH) basés sur la distribution de Hypertabastic. Ces deux modèles sont proposés pour étudier la distribution de l’analyse de la duré de survie en comparaison avec d’autre modèles paramétriques. Nous avons validé ces modèles paramétriques en utilisantY2n. Les études de simulation ont été conçus.Dans le dernier chapitre, nous avons proposé les applications de ces modèles paramétriques à trois données de bio-médicale. Le premier a été fait les données étendues des temps de rémission des patients de leucémie aiguë qui ont été proposées par Freireich et al. sur la comparaison de deux groupes de traitement avec des informations supplémentaires sur les log du blanc du nombre de globules. Elle a montré que le modèle Hypertabastic AFT est un modèle précis pour ces données. Le second a été fait sur l’étude de tumeur cérébrale avec les patients de gliome malin, ont été proposées par Sauerbrei & Schumacher. Elle a montré que le meilleur modèle est Hypertabastic PH à l’ajout de cinq variables de signification. La troisième demande a été faite sur les données de Semenova & Bitukov, à concernant les patients de myélome multiple. Nous n’avons pas proposé un modèle exactement pour ces données. En raison de cela était les intersections de temps de survie.Par conséquent, nous vous conseillons d’utiliser un autre modèle dynamique que le modèle de la Simple Cross-Effect à installer ces données
This thesis was designed to explore the dynamic regression models, assessing the sta-tistical inference for the survival and reliability data analysis. These dynamic regressionmodels that we have been considered including the parametric proportional hazards andaccelerated failure time models contain the possibly time-dependent covariates. We dis-cussed the following problems in this thesis.At first, we presented a generalized chi-squared test statisticsY2nthat is a convenient tofit the survival and reliability data analysis in presence of three cases: complete, censoredand censored with covariates. We described in detail the theory and the mechanism to usedofY2ntest statistic in the survival and reliability data analysis. Next, we considered theflexible parametric models, evaluating the statistical significance of them by usingY2nandlog-likelihood test statistics. These parametric models include the accelerated failure time(AFT) and a proportional hazards (PH) models based on the Hypertabastic distribution.These two models are proposed to investigate the distribution of the survival and reliabilitydata in comparison with some other parametric models. The simulation studies were de-signed, to demonstrate the asymptotically normally distributed of the maximum likelihood estimators of Hypertabastic’s parameter, to validate of the asymptotically property of Y2n test statistic for Hypertabastic distribution when the right censoring probability equal 0% and 20%.n the last chapter, we applied those two parametric models above to three scenes ofthe real-life data. The first one was done the data set given by Freireich et al. on thecomparison of two treatment groups with additional information about log white blood cellcount, to test the ability of a therapy to prolong the remission times of the acute leukemiapatients. It showed that Hypertabastic AFT model is an accurate model for this dataset.The second one was done on the brain tumour study with malignant glioma patients, givenby Sauerbrei & Schumacher. It showed that the best model is Hypertabastic PH onadding five significance covariates. The third application was done on the data set given by Semenova & Bitukov on the survival times of the multiple myeloma patients. We did not propose an exactly model for this dataset. Because of that was an existing oneintersection of survival times. We, therefore, suggest fitting other dynamic model as SimpleCross-Effect model for this dataset

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Zhao, Feng. "Bootstrap variable selection and model validation for Cox's proportional hazards regression models, with applications to the identification of factors predictive of overall and post-relapse survival in advanced epithelial ovarian cancer." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape17/PQDD_0026/MQ31275.pdf.

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Bersot, Vitor Fernandes. "Mudança temporal do aleitamento materno exclusivo na América Latina e Caribe: atualização de seus determinantes e da tendência secular." Universidade de São Paulo, 2011. http://www.teses.usp.br/teses/disponiveis/6/6138/tde-28092011-153100/.

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Introdução: Os múltiplos e interativos efeitos protetores do aleitamento materno exclusivo (AME) na saúde e sobrevivência infantil justificam as recomendações universais para promover sua prática. Poucos são os estudos que avaliam a tendência do padrão do AME entre países. Objetivo: Analisar a mudança temporal do AME em cinco países da América Latina e Caribe (ALC) comparando dados das décadas de 1990 e 2000. Métodos: A dissertação é composta por um manuscrito, que avaliou dados de crianças de 0 a 6 meses incluídas nas amostras das pesquisas Demographic Health Survey conduzidas em Brasil, Colômbia, Haiti, Peru e República Dominicana. Foram estimadas as prevalências do AME e suas taxas anuais de variação ponderada, segundo país e ano de inquérito. A duração do AME foi estimada usando a análise de sobrevida de Kaplan-Meier, considerando a idade atual da criança como o tempo de sobrevida e o AME como variável binária, referente à situação da prática no momento da entrevista. As curvas de sobrevivência foram construídas por país, em cada década, e a comparação entre elas usou o teste log-rank. A mediana do tempo de amamentação foi calculada para cada variável independente e a relação entre essas variáveis e o desmame até os seis meses foi analisada pela técnica de regressão de Cox com modelo múltiplo. Resultados: A prevalência de AME aumentou em quatro dos cinco países estudados, com incremento ao ano mais marcante na Colômbia (11 por cento ) e no Haiti (17 por cento ). A duração mediana apresentou duas tendências de evolução: aumento com equidade na Colômbia e no Haiti, e estagnação com distribuição desigual entre os subgrupos populacionais da última década no Brasil, Peru e República Dominicana. No modelo múltiplo de regressão, variáveis de demografia e do perfil de uso dos serviços de saúde associaram-se à duração do AME. A residência em área rural foi a variável reiteradamente associada, de forma negativa no Brasil (HR=1,68; IC 95 por cento :1,06-2,67) e na Colômbia (HR=1,39; IC 95 por cento :1,03-1,87), enquanto que positivamente no Peru (HR=0,40; IC 95 por cento :0,19-0,83). Conclusão: O balanço da tendência do AME na ALC é positivo, embora não uniforme ao longo das duas décadas analisadas. Os achados sinalizam a necessidade de intervenções para a promoção do AME que levem em consideração a localização geográfica das famílias e a qualidade prestada nos serviços de saúde
Introduction: Multiple and interactive protective effects of exclusive breastfeeding (EBF) in health and child survival justify recommendations for promoting universal practice. There are few studies that assess the tendency of the pattern of EBF between countries. Objective: To analyze the temporal change of the AME in five countries in Latin America and Caribbean (LAC) comparing data from 1990 and 2000 decades. Methods: The dissertation consists of a manuscript, which evaluated data from children aged 0 to 6 months in the samples of the Demographic Health Survey conducted research in Brazil, Colombia, Haiti, Peru and the Dominican Republic. Were estimated the prevalence of exclusive breastfeeding and its weighted annual rates of change, according to country and survey year. The duration of EBF was estimated using survival analysis Kaplan-Meier method, considering the current age of the child as the survival time and EBF as binary variable, concerning the state of practice at the time of the interview. The survival curves were constructed for each country, in every decade, and the comparison between them used the log-rank test. The median duration of breastfeeding was calculated for each independent variable and the relationship between these variables and weaning at six months was analyzed using Cox regression model. Results: The prevalence of EBF increased in four of the five countries studied, increasing the most remarkable years in Colombia (II per cent ) and Haiti (17 per cent ). The median duration of evolution showed two trends: growth with equity in Colombia and Haiti, and stagnation with unequal distribution among the population subgroups of the last decade in Brazil, Peru and the Dominican Republic. In the multiple model of regression variables and the demographic profile of use of health services were associated with duration of EBF. The residence in a rural area was the variable consistently associated negatively in Brazil (HR = 1.68, CI 95 per cent : 1,06-2,67) and Colombia (HR = 1.39, CI 95 per cent : 1,03-1,87), while positively in Peru (HR = 0.40, CI 95 per cent : 0,19-0,83). Conclusion: The balance of the trend of EBF in LAC is positive, though not uniform throughout the two decades analyzed. The findings suggest the need for interventions for the promotion of exclusive breastfeeding taking into account the geographical location of families and provided quality health services

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Choi, Ickwon. "Computational Modeling for Censored Time to Event Data Using Data Integration in Biomedical Research." Case Western Reserve University School of Graduate Studies / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=case1307969890.

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Lukaševičiūtė, Daiva. "Regresiniai modeliai išgyvenamumo analizėje ir jų taikymas ligonių, sergančių reumatoidiniu artritu, mirtingumo analizei." Master's thesis, Lithuanian Academic Libraries Network (LABT), 2010. http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2009~D_20101125_190734-77321.

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Darbo metu buvo išnagrinėta įvairių faktorių (kovariančių) įtaka reumatoidiniu artritu sergančio 531 ligonio mirtingumui. Buvo taikomas vienas iš regresinių išgyvenamumo modelių – Cox’o modelis. Iš minėtos 531 ligonio imties mirę buvo 32 ligoniai. Iš pradžių buvo tiriama ligonių imtis laiko nuo ligos pradžios aspektu. Šiuo atveju prognozuojantys veiksniai buvo amžius, kada liga buvo diagnozuota (AMZDGN), lytis (LYTKOD), gydymas Metotreksatu (GYD_MTX) ir gydymas Azatriopinu/Imuranu (AZA_IMUR). Vėliau, tiriant ligonių mirtingumą kaip amžiaus funkciją, nustatyti svarbiausi lemiantys veiksniai buvo šie: ligonių lytis (LYTKOD) ir gydymas Azatriopinu/Imuranu (AZA_IMUR). Gauti rezultatai, t.y. ligonių išgyvenamumą lemiančios kovariantės (veiksniai), beveik visiškai sutampa su gydytojų nurodytais. Tai dar kartą patvirtina matematinių statistinių modelių, šiuo atveju nagrinėjamo Cox‘o modelio, taikymo realiame gyvenime, svarbą. Kitai duomenų imčiai, t.y. vėžiu sergančių ligonių duomenų aibei, buvo taikomas Persikertančių mirimų intensyvumų (SCE) modelis, t.y. tikrinama Cox‘o modelio adekvatumo duomenims hipotezė. Hipotezė buvo atmesta, nes minėtiems duomenims Cox‘o modelis negalioja. Pagrindinis darbo rezultatas yra šis: gautas kriterijus Cox‘o modelio adekvatumui tikrinti, naudojant nupjautus iš kairės ir cenzūruotus iš dešinės duomenis, sudarytos programos kriterijui realizuoti. Reumatoidinio artrito ligonių duomenų aibei, t.y. nupjautiems iš kairės ir cenzūruotiems iš dešinės... [toliau žr. visą tekstą]
In this work the Cox proportional hazards model was applied to investigate the influence of various factors (covariates) to mortality of rheumatoid arthritis patients of Vilnius. In the first case, the sample of 531 patients was analysed. Analysing survival of patients of the sample as function of time from the beginnig of the disease, the prognostic factors were LYTKOD (the sex of patients), AMZDGN (patients‘ age, when the rheumatoid arthritis was diagnosed), GYD_MTX (treatment with metotrexat) and AZA_IMUR (treatment with Azatriopin/Imuran). When survival was analysed as function of age then the prognostic factor were LYTKOD (the sex of patients) and AZA_IMUR (treatment with Azatriopin/Imuran). The results are almost identical to those, which doctors suggested. This fact confirms the importance of using mathematical statistical models to solve the problems of the real life. In this case, the importance of using the Cox model. On the other hand, Simple cross-effects (SCE) model was aplied for the sample of canser patients. In the case of this model the hypothesis of Cox model fiting for canser patients‘ data was rejected. The most important result of this work is that the criterion of Cox model fitting to left truncated and right censored data was constructed. Also a program of SAS for the criterion was created. The the hypothesis of Cox model fiting for the rheumatoid arthritis patients wasn‘t rejected, because Cox model fit for these data.

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Sjöström, Lars. "Differences in age at breeding between two genetically different populations of brown trout (Salmo trutta)." Thesis, Uppsala universitet, Statistiska institutionen, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-388613.

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Survival analysis is an effective tool for conservation studies, since it measure the risk of an event that is important for the survival of populations and preservation of biodiversity. In this thesis three different models for survival analysis are used to estimate the age at breeding between two genetically different populations of brown trout. These populations are an evolutionary enigma, since they apparently coexist in direct competition with each other, which according to ecological theory should not happen. Thus it is of interest if differences between them can be identified. The data consists of brown trouts and has been collected over 20 years. The models are the Cox Proportional Hazards model, the Complementary Log-Log Link model and the Log Logistic Accelerated Failure-Time model. The Cox model were estimated in three different ways due to the nonproportional hazards in the estimates of time to breeding, which gave different interpretations of the same model. All of the models agree that the population B breed at younger ages than the population A, which suggests that the two populations have different reproductive strategies.

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Irobi, Edward Okezie. "Time to Diagnosis of Second Primary Cancers among Patients with Breast Cancer." ScholarWorks, 2016. https://scholarworks.waldenu.edu/dissertations/2661.

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Many breast cancer diagnoses and second cancers are associated with BRCA gene mutations. Early detection of cancer is necessary to improve health outcomes, particularly with second cancers. Little is known about the influence of risk factors on time to diagnosis of second primary cancers after diagnosis with BRCA-related breast cancer. The purpose of this cohort study was to examine the risk of diagnosis of second primary cancers among women diagnosed with breast cancer after adjusting for BRCA status, age, and ethnicity. The study was guided by the empirical evidence supporting the mechanism of action in the mutation of BRCA leading to the development of cancer. Composite endpoint was used to define second primary cancer occurrences, and Kaplan-Meier survival curves were used to compare the median time-to-event among comparison groups and BRCA gene mutation status. Cox proportional hazards was used to examine the relationships between age at diagnosis, ethnicity, BRCA gene mutation status, and diagnosis of a second primary cancer. The overall median time to event for diagnosis of second primary cancers was 14 years. The hazard ratios for BRCA2 = 1.47, 95% CI [1.03 - 2.11], White = 1.511, 95% CI [1.18 - 1.94], and American Indian/Hawaiian = 1.424, 95% CI [1.12 -1.81] showing positive significant associations between BRCA2 mutation status and risk of diagnosis of second primary colorectal, endometrial, cervical, kidney, thyroid, and bladder cancers. Data on risk factors for development of second cancers would allow for identification of appropriate and timely screening procedures, determining the best course of action for prevention and treatment, and improving quality of life among breast cancer survivors.

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Callas, Peter W. "Empirical comparisons of logistic regression, Poisson regression, and Cox proportional hazards modeling in analysis of occupational cohort data." 1994. https://scholarworks.umass.edu/dissertations/AAI9510451.

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Three multiplicative models commonly used in the analysis of occupational cohort studies are logistic, Poisson, and Cox proportional hazards regression. Although the underlying theories behind these are well known, this has not always led to clear decisions for selecting which to use in practice. This research was conducted to examine the effect model choice has on the epidemiologic interpretation of occupational cohort data. The three models were applied to a National Cancer Institute historical cohort of formaldehyde-exposed workers. Samples were taken from this dataset to create scenarios for model comparisons, varying the study size (n = 600, 3000, 6000), proportion of subjects experiencing the outcome (2.5%, 10%, 50%), strength of association between exposure and outcome (weak, moderate, strong), follow-up length (5, 15, 30 years), and proportion of subjects lost to follow-up (0%, 10%, 17.5%). Other factors investigated included how to handle subjects lost to follow-up in logistic regression. Models were compared on risk estimates, confidence intervals, and practical issues such as ease of use. The Poisson and Cox models yielded nearly identical relative risks and confidence intervals in all situations except when confounding by age could not be closely controlled in the Poisson analysis, which occurred when the sample size was small or outcome was rare. Logistic regression findings were more variable, with risk estimates differing most from the Cox results when there was a common outcome or strong relative risk. Logistic was also less precise than the others. Thus, although logistic was the easiest model to implement, it should only be used in occupational cohort studies when the outcome is rare (5% or less), and the relative risk is less than about 2. Even then, since it does not account for follow-up time differences between subjects or changes in risk factors values over time, the Cox or Poisson models are better choices. Selecting between these can usually be based on convenience, except when confounding cannot be closely controlled in the Poisson model but can in the Cox model, or when the Poisson assumption of exponential baseline survival is not met. In these cases Cox should be used.

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Liu, Chia-Chiung, and 劉佳峻. "A simulation study for cut points analysis in Logist regression and Cox proportional hazards model." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/yn3698.

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碩士
國立中山大學
應用數學系研究所
107
In clinical practice, it is often necessary to segment the continuous variables for risk assessment, that is, to convert the continuous prognostic factors into categories to facilitate clinical judgment and interpretation. There are three problems to be solved in the study of estimating cut points. The first problem is to determine the optimal number of cut points. In the traditional methods, many of them have been developed to find one optimal cut point to categorize variables into two subgroup. However, in a lot of situations, finding more than one cut points is of interest. The second one is to find the location of optimal cut points. The last one is the statistical inferences after finding the optimal number and locations of cut points, including correcting the p-value, relative risks, powers, etc. 　　In previous theses(Tsai, Y.H.(2018), and Chiu, Y.C.(2018)), they proposed a new approach in both the logistic and Cox regression models, combining the cross-validation and Monte Carlo methods(CVM), to find the optimal number and locations of cut points. However, in their theses, the proposed method was not compared with other methods. In this thesis, we conducted simulation studies to compare the proposed CVM with three other methods, including naive approach(without any correction,NA), split-sample approach(SS), and cross-validation approach(CV). We compares the performance between these four methods in estimating the number and location s of cut points, relative risks, and powers in both univariate and multivariate analysis and for different sample sizes.

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Hsiao, Han, and 蕭涵. "Analysis of high dimensional gene expression and mutation data in bladder cancer using Cox proportional hazards model and logistic regression via different penalizations." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/amf4n9.

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碩士
國立中山大學
應用數學系研究所
106
Bladder cancer is one of the malignant diseases in urinary system. Its common symptoms include hematuria which could be seen through eyes or urine analysis. In order to understand the effect of gene expression and mutation data on subtypes and recurrent event in patients with bladder cancer, we downloaded data from The Cancer Genome Atlas (TCGA) and applied high-dimensional analysis such as LASSO, Ridge, Adaptive Lasso and Cox model to screen gene variables, compare the performance of different models and predict the hazard of each patients. Among the selected gene candidates, we found TP53 and ERBB3 have been published in quite a few papers, which could verify our method. Not only the list of genes could help the lab to perform further analysis but also it could screen out the potential patients in advance. On the other hand, we also wrote some functions to access and deal with gene database in R language, which could be used by other researchers in the future.

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Joy, Nathaniel Allen. "A Duration Analysis of Food Safety Recall Events in the United States: January, 2000 to October, 2009." Thesis, 2010. http://hdl.handle.net/1969.1/ETD-TAMU-2010-12-8826.

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The safety of the food supply in the United States has become an issue of prominence in the minds of ordinary Americans. Several government agencies, including the United States Department of Agriculture and the Food and Drug Administration, are charged with the responsibility of preserving the safety of the food supply. Food is withdrawn from the market in a product recall when tainted or mislabeled and has the potential to harm the consumer in some manner. This research examines recall events issued by firms over the period of January, 2000 through October, 2009 in the United States. Utilizing economic and management theory to establish predictions, this study employs the Cox proportional hazard regression model to analyze the effects of firm size and branding on the risk of recall recurrence. The size of the firm was measured in both billions of dollars of sales and in thousands of employees. Branding by the firm was measured as a binary variable that expressed if a firm had a brand and as a count of the number of brands within a firm. This study also provides a descriptive statistical analysis and several findings based on the recall data specifically relating to annual occurrences, geographical locations of the firms involved, types of products recalled, and reasons for recall. We hypothesized that the increasing firm size would be associated with increased relative risk of a recall event while branding and an increasing portfolio of brands would be associated with decreased relative risk of a recall event. However, it was found that increased firm size and branding by the firm are associated with an increased risk of recall occurrence. The results of this research can have implications on food safety standards in both the public and private sectors.

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Martínez, Vargas Danae Mirel. "Régression de Cox avec partitions latentes issues du modèle de Potts." Thèse, 2019. http://hdl.handle.net/1866/22552.

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Chen, Yun. "False selection rate methods in the Cox Proportional Hazards Model." 2006. http://www.lib.ncsu.edu/theses/available/etd-07232006-111640/unrestricted/etd.pdf.

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Dissertations / Theses on the topic 'Cox proportional hazards regression'

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