Relevant bibliographies by topics / Cox Proportional Hazard Regression Model

Academic literature on the topic 'Cox Proportional Hazard Regression Model'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Cox Proportional Hazard Regression Model"

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Khinanti, Aprilia Sekar, Sudarno Sudarno, and Triastuti Wuryandari. "MODEL REGRESI COX PROPORTIONAL HAZARD PADA DATA KETAHANAN HIDUP PASIEN HEMODIALISA." Jurnal Gaussian 10, no. 2 (May 31, 2021): 303–14. http://dx.doi.org/10.14710/j.gauss.v10i2.30958.

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Cox regression is a type of survival analysis that can be implemented with proportional hazard models or duration models. In the survival analysis data, there is a possibility that the data has ties, so it is necessary to use several approaches in estimating the parameters, namely the breslow, efron, and exact approaches. In this study, the Cox proportional hazard regression was used as a method of analysis for knowing the factors that influence the survival time on chronic kidney patients undergoing hemodialysis therapy. Based on the analysis that has been done, the best model is obtained with an exact approach and the factors that influence the survival time of hemodialysis patients are systolic blood pressure, hemoglobin level, and dialysis time. Hemodialysis patients who have high systolic blood pressure have a chance of failing to survive 12,950 times than normal systolic blood pressure.While the hemodialysis patient hemoglobin level increases, the hemodialysis patients chances of failing to survive is 0,6681 times less. Hemodialysis patients who received dialysis therapy with a dialysis time of more than four hours had 0.237 times the chance of failing to survive than patients with a dialysis time of less than or equal to 4 hours.Keywords: Cox Regression ,Survival, Ties, Hemodialysis.
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SUDANA, I. GEDE ARI, NI LUH PUTU SUCIPTAWATI, and LUH PUTU IDA HARINI. "PENERAPAN REGRESI COX PROPORTIONAL HAZARD UNTUK MENDUGA FAKTOR-FAKTOR YANG MEMENGARUHI LAMA MENCARI KERJA." E-Jurnal Matematika 2, no. 3 (August 30, 2013): 7. http://dx.doi.org/10.24843/mtk.2013.v02.i03.p041.

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Survival analysis is a statistical method that accommodates the collection of censored data. One of popular method in survival analysis is the Cox Proportional Hazard Regression. The Cox Proportional Hazard Regression can be used to see old looking for work where data may contain censored data. This article aims investigate the characteristics of job seekers and the variables that affect old looking for work. To establish the best model using Stepwise Selection method. Prior to that the assumption of Cox Proportional Hazards Regression is tested using log minus log curve. The results obtained from Cox Proportional Hazards Regression model is as follows
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Wuryandari, Triastuti, Sri Haryatmi Kartiko, and Danardono Danardono. "ANALISIS SURVIVAL UNTUK DURASI PROSES KELAHIRAN MENGGUNAKAN MODEL REGRESI HAZARD ADDITIF." Jurnal Gaussian 9, no. 4 (December 7, 2020): 402–10. http://dx.doi.org/10.14710/j.gauss.v9i4.29259.

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Survival data is the length of time until an event occurs. If the survival time is affected by other factor, it can be modeled with a regression model. The regression model for survival data is commonly based on the Cox proportional hazard model. In the Cox proportional hazard model, the covariate effect act multiplicatively on unknown baseline hazard. Alternative to the multiplicative hazard model is the additive hazard model. One of the additive hazard models is the semiparametric additive hazard model that introduced by Lin Ying in 1994. The regression coefficient estimates in this model mimic the scoring equation in the Cox model. Score equation of Cox model is the derivative of the Partial Likelihood and methods to maximize partial likelihood with Newton Raphson iterasi. Subject from this paper is describe the multiplicative and additive hazard model that applied to the duration of the birth process. The data is obtained from two different clinics,there are clinic that applies gentlebirth method while the other one no gentlebirth. From the data processing obtained the factors that affect on the duration of the birth process are baby’s weight, baby’s height and method of birth. Keywords: survival, additive hazard model, cox proportional hazard, partial likelihood, gentlebirth, duration
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Xue, Xiaonan, Xianhong Xie, and Howard D. Strickler. "A censored quantile regression approach for the analysis of time to event data." Statistical Methods in Medical Research 27, no. 3 (May 10, 2016): 955–65. http://dx.doi.org/10.1177/0962280216648724.

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The commonly used statistical model for studying time to event data, the Cox proportional hazards model, is limited by the assumption of a constant hazard ratio over time (i.e., proportionality), and the fact that it models the hazard rate rather than the survival time directly. The censored quantile regression model, defined on the quantiles of time to event, provides an alternative that is more flexible and interpretable. However, the censored quantile regression model has not been widely adopted in clinical research, due to the complexity involved in interpreting its results properly and consequently the difficulty to appreciate its advantages over the Cox proportional hazards model, as well as the absence of adequate validation procedure. In this paper, we addressed these limitations by (1) using both simulated examples and data from National Wilms’ Tumor clinical trials to illustrate proper interpretation of the censored quantile regression model and the differences and the advantages of the model compared to the Cox proportional hazards model; and (2) developing a validation procedure for the predictive censored quantile regression model. The performance of this procedure was examined using simulation studies. Overall, we recommend the use of censored quantile regression model, which permits a more sensitive analysis of time to event data together with the Cox proportional hazards model.
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Xie, Xianhong, Howard D. Strickler, and Xiaonan Xue. "Additive Hazard Regression Models: An Application to the Natural History of Human Papillomavirus." Computational and Mathematical Methods in Medicine 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/796270.

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There are several statistical methods for time-to-event analysis, among which is the Cox proportional hazards model that is most commonly used. However, when the absolute change in risk, instead of the risk ratio, is of primary interest or when the proportional hazard assumption for the Cox proportional hazards model is violated, an additive hazard regression model may be more appropriate. In this paper, we give an overview of this approach and then apply a semiparametric as well as a nonparametric additive model to a data set from a study of the natural history of human papillomavirus (HPV) in HIV-positive and HIV-negative women. The results from the semiparametric model indicated on average an additional 14 oncogenic HPV infections per 100 woman-years related to CD4 count < 200 relative to HIV-negative women, and those from the nonparametric additive model showed an additional 40 oncogenic HPV infections per 100 women over 5 years of followup, while the estimated hazard ratio in the Cox model was 3.82. Although the Cox model can provide a better understanding of the exposure disease association, the additive model is often more useful for public health planning and intervention.
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Baisaku, Nurul Azizah, Jajang Jajang, and Nunung Nurhayati. "ANALISIS SURVIVAL DENGAN COX PROPORTIONAL HAZARD PADA KASUS DEMAM TIFOID." Majalah Ilmiah Matematika dan Statistika 22, no. 1 (March 13, 2022): 1. http://dx.doi.org/10.19184/mims.v22i1.29325.

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A common problem found in survival data is the presence of censored data. The length of hospitalization of Typhoid fever patients until declared cured is one of example of this data. Here, we use Cox regression model to analysis this data. Partial likelihood is one of the methods of estimating parameters for Cox regression model. In many cases of censored data, two objects (patients) have the same length of hospitalization (ties). Therefore, to estimate the parameters of the model must use the right method. Here we used partial likelihood Breslow, Efron, and Exact methods. The study was motivated by how the three methods performed for Cox regression model. The data used for the implementation of these methods is length of hospitalization of Typhoid fever patients at Mekar Sari Hospital-Bekasi in 2020. Based on AIC criteria, we found that exact method is the best model (minimum AIC) for parameter estimation of Cox regression model. Referring to the Cox regression model by using a significance level of 10%, there are five predictor variables that affects the length of patient hospitalization. The five variables are age, vomiting, dirty tongue, hemoglobin, and leukocyte.Keywords: Typhoid fever, Cox regression, Breslow method, Efron method, exact method.MSC2020: 62N02, 62N03
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Abidin, Haykal, Novita Eka Chandra, and Mohammad Syaiful Pradana. "Pemodelan Regresi Cox Proportional Hazard Pada Data Perceraian." Unisda Journal of Mathematics and Computer Science (UJMC) 6, no. 2 (December 30, 2020): 49–58. http://dx.doi.org/10.52166/ujmc.v6i2.2393.

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The purpose of this research is modeling the Cox proportional hazard regression form on divorce data in Pelaihari sub-district, Tanah Laut district, South Kalimantan province. The source of the data comes from the Court Decision in Pelaihari District, Tanah Laut Regency, South Kalimantan. The data analysis technique uses software R with the steps, namely data description, Log-Rank test, checking proportional hazard assumptions, Cox regression model parameter estimation, backward selection with AIC, the best model parameter significance test, calculating Hazard ratio and interpretation of each predictor variable. Based on the results of the analysis and discussion, it was found that for the Log-Rank test, the variable survival time for domestic violence, forced marriage, lying and stories of disgrace differed significantly. While the model that meets the criteria after iteration up to 15 times is the 15th model with the smallest AIC value and p-value <0.05 with factors that significantly influence divorce in Pelaihari sub-district based on modeling results using Cox proportional Hazard regression. are the variables of cheating, gambling, domestic violence, forced marriage, lies, jealousy and disgrace story variables
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Sanusi, Wahidah, A. Alimuddin, and S. Sukmawati. "Model Regresi Cox dan Aplikasinya dalam Menganalisis Ketahanan Hidup Pasien Penderita Diabetes Mellitus di Rumah Sakit Bhayangkara Makassar." Journal of Mathematics, Computations, and Statistics 1, no. 1 (May 17, 2019): 62. http://dx.doi.org/10.35580/jmathcos.v1i1.9180.

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Abstrak. Analisis tahan hidup adalah salah satu prosedur statistik untuk melakukan analisa data berupa waktu tahan hidup dan variabel yang mempengaruhi waktu tahan hidup. Pada penelitian ini analisis tahan hidup diaplikasikan pada kasus diabetes mellitus di Rumah Sakit Bhayangkara Makassar pada tahun 2016. Salah satu metode analisis tahan hidup yang digunakan adalah model Regresi Cox Proporsional Hazard. Penggunaan model regresi cox proporsional hazard harus memenuhi asumsi proporsional hazard. Penelitian ini juga menggunakan distribusi eksponensial dua parameter untuk menentukan fungsi hazard dan metode Breslow dalam membentuk model cox terbaik. Dari hasil penelitian diperoleh faktor-faktor signifikan yang mempengaruhi waktu tahan hidup adalah umur dan kadar gula darah, namun faktor kadar gula darah tidak memenuhi asumsi proporsional hazard, sehingga digunakan Model Cox Extended untuk memperbaiki model cox proporsional hazard. Covariate yang tidak memenuhi asumsi proporsional hazard dalam model cox extended dinteraksikan dengan fungsi waktu . Model Cox Extended pada akhirnya memberikan informasi tentang faktor -faktor yang berpengaruh signifikan terhadap waktu tahan hidup yaitu umur dan kadar gula darah terikat waktu, dimana setiap individu yang berumur kurang dari 45 tahun memiliki resiko kegagalan 0,015 kali lebih kecil dibandingkan dengan pasien yang berumur lebih dari 45 tahun dan individu yang kadar gula darahnya tinggi memiliki resiko kegagalan sebesar 1,128 kali lebih besar dibandingkan dengan pasien yang memiliki kadar gula darah rendah dan normal.Kata Kunci: Analisis Tahan Hidup, Regresi Cox Proporsional Hazard, Diabetes Mellitus, Model Cox ExtendedAbstract. Survival analyze is one of the statistical procedures to analyze data survival time and variable that will affect the rate of recovery of patients. In this research, survival analyze was applicated by diabetes mellitus case in Bhayangkara Hospital Makassar 2016. One of the methods survival analyze used is cox regression model with proportional hazard. The use of cox regression model with proportional hazard must fulfill assumption of proportional hazard. This research also use 2-parameter exponential distribution to determine of hazard function and Breslow method to shaping the best of cox model. From the results of the research give conclusion that factors affecting of time recovery are age and blood sugar level. But the blood sugar level factor does not fulfill the proportional hazard assumptions. So that the extended cox model was used to improve the cox proportional hazard model. Variables that does not fulfill the proportional hazard assumption in the extended cox model are interacted with the time function . Finally, the extended cox model give information about the factors most affect the rate of recovery are age and time bound blood sugar level. Every individual less than 45 years old has a 0,015 times greater risk of failure than patients older than 45 years old and individuals with high blood sugar level had a risk of failure is 1,128 times greater than the low and normal blood sugar level Keywords: Survival Analyze, Cox Proportional Hazard Regression, Diabetes Mellitus, Extended Cox Model
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Hosseinioun, Nargess. "Cox Proportional Hazard Regression for Risk Factors of Alzheimer’s Disease." Journal of Medicine 20, no. 2 (June 27, 2019): 72–79. http://dx.doi.org/10.3329/jom.v20i2.42006.

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Background: Alzheimer’s disease is a form of dementia, mainly strikes people in their 60 and 70s. While no one cause has been determined, researchers have identified certain factors which may put people at a higher risk of developing the disease. Iran’s Alzheimer Association has announced that more than 35% of people over 80 years old suffers from this problem across the country. This paper aims to investigate modifiable risk factors of Alzheimer’s disease based on Kaplan-Meier estimator and Cox regression analysis (proportional hazard model) and to find out which factors are related to developing of this disease. Materials & methods: Residents newly admitted to nursing homes with a diagnosis of probable Alzheimer’s disease have been considered. Patients were at least 75 years of age from 9 Medicare/ Medicaid certified nursing homes of Khorasan and Tehran states and we excluded patients with a history of mental retardation, mental illness or any other long-life mental health disorders. This strategy yielded a sample of 115 patients (37 Males and 78 Females) with total death rate of 47%. Results: Our findings demonstrate that Age, Gender, Heritage, Apoplexy, Mental and Physical Activities statistically affect Survival and also Hazard Rate, In contrast, we found no link between survival duration and a history of Addiction and Blood Pressure. Conclusions: These findings have implications for clinical practice and intervention strategies in medical and public health. J MEDICINE JUL 2019; 20 (2) : 72-79
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Fajarini, Firda Anisa, and Mohamat Fatekurohman. "Analisis Premi Asuransi Jiwa Menggunakan Model Cox Proportional Hazard." Indonesian Journal of Applied Statistics 1, no. 2 (March 13, 2019): 88. http://dx.doi.org/10.13057/ijas.v1i2.25280.

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<p>Cox proportional hazard model is a regression model that is used to see the factors that cause an event. The survival analysis used in this research is the period of time the client is able to pay the life insurance premium using Cox proportional hazard model with Breslow method.The purpose of this research is to know how sex, age, insured money, job, method of payment of premium, premium, and type of product can influence the level of ability of client to make payment of life insurance premium based on customer data from PT. BRI Life Insurance Branch of Jember in 2007.The result of this research is the final model of Cox proportional hazard obtained from several variables which have significant influence with simultaneous and partial significance test is the variable of insured money (<em>X<sub>3</sub></em>), variable of payment method of premium (<em>X<sub>5</sub></em>), premium variable (<em>X<sub>6</sub></em>) , and insurance product variable (<em>X<sub>7</sub></em>) . The four variables are said to have a significant effect on the model, so that the final model of Cox proportional hazard is obtained that consists of the parameter estimation (<em>β</em>) value of each variable</p><p> </p><p><strong>Keywords</strong><strong> : </strong>survival analysis; cox proportional hazard model; breslow method; life insurance.</p>
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Dissertations / Theses on the topic "Cox Proportional Hazard Regression Model"

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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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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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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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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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Gwaze, Arnold Rumosa. "A cox proportional hazard model for mid-point imputed interval censored data." Thesis, University of Fort Hare, 2011. http://hdl.handle.net/10353/385.

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There has been an increasing interest in survival analysis with interval-censored data, where the event of interest (such as infection with a disease) is not observed exactly but only known to happen between two examination times. However, because so much research has been focused on right-censored data, so many statistical tests and techniques are available for right-censoring methods, hence interval-censoring methods are not as abundant as those for right-censored data. In this study, right-censoring methods are used to fit a proportional hazards model to some interval-censored data. Transformation of the interval-censored observations was done using a method called mid-point imputation, a method which assumes that an event occurs at some midpoint of its recorded interval. Results obtained gave conservative regression estimates but a comparison with the conventional methods showed that the estimates were not significantly different. However, the censoring mechanism and interval lengths should be given serious consideration before deciding on using mid-point imputation on interval-censored data.
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Sandström, Caroline, and Karl Norling. "Female longevity : A survival analysis on 19th century women using the Cox Proportional Hazard model." Thesis, Umeå universitet, Statistiska institutionen, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-49700.

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Minya, Kristoffer. "Överlevnadsanalys i tjänsteverksamhet : Tidspåverkan i överklagandeprocessen på Migrationsverket." Thesis, Linköpings universitet, Statistik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-110428.

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Migrationsverket är en myndighet som prövar ansökningar från personer som vill söka skydd, ha medborgarskap, studera eller vill jobba i Sverige. Då det på senare tid varit en stor ökning i dessa ansökningar har tiden för vilket ett beslut tar ökat. Varje typ av ansökning (exempelvis medborgarskap) är en process som består av flera steg. Hur beslutet går igenom dessa steg kallas för flöde. Migrationsverket vill därför öka sin flödeseffektivitet. När beslutet är klart och personen tagit del av det men inte är nöjd kan denne överklaga. Detta är en av de mest komplexa processerna på Migrationsverket. Syftet är analysera hur lång tid denna process tar och vilka steg i processen som påverkar tiden. Ett steg (som senare visar sig ha en stor effekt på tiden) är yttranden. Det är när domstolen begär information om vad personen som överklagar har att säga om varför denne överklagar. För att analysera detta var två metoder relevanta, accelerated failure time (AFT) och \multi-state models (MSM). Den ena kan predicera tid till händelse (AFT) medan den andra kan analysera effekten av tidspåverkan (MSM) i stegen. Yttranden tidigt i processen har stor betydelse för hur snabbt en överklagan får en dom samtidigt som att antal yttranden ökar tiden enormt. Det finns andra faktorer som påverkar tiden men inte i så stor grad som yttranden. Då yttranden tidigt i processen samtidigt som antal yttranden har betydelse kan flödeseffektiviteten ökas med att ta tid på sig att skriva ett informativt yttrande som gör att domstolen inte behöver begära flera yttranden.
The Swedish Migration Board is an agency that review applications from individuals who wish to seek shelter, have citizenship, study or want to work in Sweden. In recent time there has been a large increase in applications and the time for which a decision is made has increased. Each type of application (such as citizenship) is a process consisting of several stages. How the decision is going through these steps is called flow. The Swedish Migration Board would therefore like to increase their flow efficiency. When the decision is made and the person has take part of it but is not satisfied, he can appeal. This is one of the most complex processes at the Board. The aim is to analyze how long this process will take and what steps in the process affects the time. One step (which was later found to have a significant effect on time) is opinions. This is when the court requests information on what the person is appealing has to say about why he is appealing. To analyze this, two methods were relevant, accelerated failure time (AFT) and the multi-state models (MSM). One can predict time to event (AFT), the other to analyze the effect of time-manipulation (MSM) in the flow. Opinions early in the process is crucial to how quickly an appeal get judgment while the number of opinions increases the time enormously. There are other factors that affect the time but not so much as opinions. The flow efficiency can be increased by taking time to write an informative opinion which allows the court need not to ask for more opinions.
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Sposito, Ítalo Beltrão. "Continuidade e mudança na política externa dos estados latino-americanos (1945-2008)." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/101/101131/tde-28032016-141512/.

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: Este trabalho tem como objeto o redirecionamento na política externa (RPE) - conceituado como as mudanças mais radicais, abrangentes e rápidas em política externa. Para analisar este fenômeno, são buscadas as principais condições conjunturais que podem aumentar a chance de ocorrência deste evento. Estas condições estão relacionadas ao conceito de Janela Política, que representa o período em que é rompida a inércia política e os tomadores de decisões têm condições de iniciar um processo de RPE. Objetivo: encontrar e delimitar quais as condições conjunturais que aumentam as chances de ocorrência de um RPE. Método: são utilizadas ferramentas metodológicas qualitativas e quantitativas. No segundo capítulo, a análise é feita por meio de um modelo de sobrevivência (Cox Proportional Hazard Model) que analisa o efeito das variáveis sobre os riscos de ocorrência do evento em foco, definido como as alterações mais extremas de comportamento nas votações da Assembleia Geral das Nações Unidas. No terceiro capítulo, é desenvolvida uma análise qualitativa histórica focando especificamente nos casos mais radicais de RPE, buscando identificar padrões comuns no desencadeamento dos processos em estudo; com base nestes casos, são desenvolvidas tipologias explicativas para identificar diferentes caminhos causais que levam ao evento em tela. Resultados: foi identificado que mudanças de regime e de líder político, no âmbito doméstico, e intervenções militares de potências estrangeiras aumentam os riscos de ocorrência de RPE; adicionalmente, a alta polarização política e a mudança de regime, a crise política doméstica com envolvimento de atores internacionais, os processos de isolamento internacional com imposição de sanções econômicas e os períodos de crise econômica com questionamento do modelo econômico vigente por parte dos atores políticos podem combinadamente levar à ocorrência de RPE. Conclusões: apesar da importância do interesses de atores políticos em empreender um projeto de RPE, foi identificado que determinados eventos aumentam os riscos deste processo ocorrer.
This thesis object is the foreign policy restructuring (FPR) - conceptualized as the most radical, encompassing, and fast changes in foreign policy. To analyze this phenomenon, there will be sought the main conjuncture conditions that might enhance the chances of this event occurrence. These conditions are related to the Policy Window concept, that represents a period during which the political inertia is disrupted and decision makers have the circumstances to undertake a FPR process. Objective: find and outline the conjectural conditions and variables that increase the chances of occurrence of a FPR. Methods: it will be used qualitative and quantitative methodological tools. In the second chapter, a survival model (Cox Proportional Hazard Model) analyses the effect variables related to the Policy Window concept over the risks of happening a FPR, defined as the most extreme changes of behavior in United Nations General Assembly roll-call votes. In the third chapter, a historical qualitative analysis is undertaken focusing exclusively on the most radical cases of FPR to develop explanatory typologies in order to identify causal conjunctures and common patters that lead to the outcome. Results: we identified that regime and political leader changes, in the national context, and military interventions by foreign powers enhance the risks of FPR occurrence; additionally, high political polarization combined with regime change, political crisis with international forces involvement, processes of international isolation with economic sanctions enforcement, and economic crises with political actors questioning the current economic model might be combined, configuring causal paths to a FPR. Conclusion: despite the importance of main political actors interest in implementing a FPR process, we identified that specific conjunctures and events raise the risks of a positive outcome.
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Book chapters on the topic "Cox Proportional Hazard Regression Model"

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Harrell, Frank E. "Cox Proportional Hazards Regression Model." In Regression Modeling Strategies, 475–519. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19425-7_20.

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Harrell, Frank E. "Cox Proportional Hazards Regression Model." In Regression Modeling Strategies, 465–507. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4757-3462-1_19.

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Lee, Mei-Ling Ting, G. A. Whitmore, and Bernard Rosner. "Benefits of Threshold Regression: A Case-Study Comparison with Cox Proportional Hazards Regression." In Mathematical and Statistical Models and Methods in Reliability, 359–70. Boston, MA: Birkhäuser Boston, 2010. http://dx.doi.org/10.1007/978-0-8176-4971-5_28.

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Zhang, Mei-Jie. "Cox Proportional Hazards Regression Models for Survival Data in Cancer Research." In Biostatistical Applications in Cancer Research, 59–70. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4757-3571-0_4.

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Abu Hasan, Nurhasniza Idham, Nor Azura Md. Ghani, Norazan Mohamed Ramli, Khairul Asri Mohd Ghani, and Khairul Izan Mohd Ghani. "Prognostic Factors for Rheumatics Heart Disease After Mitral Valve Repair Surgery Using Cox Proportional Hazard Model." In Regional Conference on Science, Technology and Social Sciences (RCSTSS 2016), 685–95. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0074-5_66.

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"The Cox Proportional Hazards Model." In Regression Models as a Tool in Medical Research, 107–18. Chapman and Hall/CRC, 2012. http://dx.doi.org/10.1201/b12925-16.

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"The Cox Proportional Hazard Regression Model and Advances." In Survival Analysis, 144–200. Chichester, UK: John Wiley & Sons, Ltd, 2012. http://dx.doi.org/10.1002/9781118307656.ch5.

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Xia, Mengying, and Leigh Wang. "Challenges and Chances of Classical Cox Regression." In Encyclopedia of Data Science and Machine Learning, 2438–49. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-9220-5.ch146.

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Cox regression is the method for investigating the effect of several variables upon the time a specified event takes to happen. It is also known as the proportional hazards regression because it is all revolved around survival analysis. The Cox proportional hazards (CPH) model is the most frequently used approach for survival analysis in a wide variety of fields. This article summarizes current research, especially its applications in the area of diagnosis and treatment of coronavirus disease 2019 (COVID-19). Also, the pros and cons of competitive machine learning (ML) models for targeting the same object will be presented.
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"Muskellunge Management: Fifty Years of Cooperation Among Anglers, Scientists, and Fisheries Biologists." In Muskellunge Management: Fifty Years of Cooperation Among Anglers, Scientists, and Fisheries Biologists, edited by Chaunte Lewis, John M. Farrell, Kelly L. Sams, Emily R. Cornwell, and Rodman G. Getchell. American Fisheries Society, 2017. http://dx.doi.org/10.47886/9781934874462.ch15.

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<em>Abstract</em>.—Viral hemorrhagic septicemia virus (VHSV) has been found in fish populations throughout the Great Lakes basin since 2003. It is a single-stranded RNA virus that affects a number of fish species, including Muskellunge <em>Esox masquinongy</em>, a major predator in these waters. The purpose of this experiment was to compare the virulence of four strains of VHSV IVb (MI03, vcG002, FPL2013-002, and FPL2014-001). Age-0 Muskellunge were randomly assigned to one of the strains and exposed to either a high (5 × 10<sup>5</sup> plaque forming units/mL) or low (5 × 104 plaque forming units/mL) dose for 1 h by immersion. Fish were then monitored for clinical signs of infection, such as petechial hemorrhages, lethargy, and death, whereupon brain and pooled organ samples were harvested using aseptic technique. Quantitative reverse transcription polymerase chain reaction assays in Muskellunge were performed along with viral isolation in order to confirm the presence of VHSV. Results of the Cox proportional hazard regression models did demonstrate a difference when comparing the time to death of the high dose versus the low dose, but no difference was observed when comparing the time to death of the four isolates over the course of the experiment. When comparing viral load in Muskellunge pooled spleen, heart, liver, and anterior and posterior kidneys or separate brain samples, there were no differences between the strains or the doses detected. Future studies with lower doses closer to the LD50 may differentiate changes in virulence properties of VHSV IVb.
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Beaudry, Catherine, and Joël Levasseur. "Collaboration, Innovation, and Funding as Survival Factors for Canadian Biotechnology SMEs." In Biotechnology, 1498–530. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-8903-7.ch062.

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This chapter aims to determine the factors, such as collaboration, research and development, intellectual property, product management and financing, that influence the survival of biotechnology firms in Canada. The research uses data from four biannual surveys on the use and development of biotechnology collected by Statistics Canada between 1999 and 2005, and follows these firms in the official business register of the organisation up to 2009, to build a Cox proportional hazard model of firm survival. The research finds that firms that collaborate for exploration purposes have better chances of survival than others. Results also suggest that a larger number of patents decreases the probability of survival. Investigation of the product development process shows that because of the vast resources necessary for clinical research, firms enter the production and commercialisation stage in a weak position, which may then result in firm exit.
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Conference papers on the topic "Cox Proportional Hazard Regression Model"

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"FAILURE PREDICTION USING THE COX PROPORTIONAL HAZARD MODEL." In 6th International Conference on Software and Data Technologies. SciTePress - Science and and Technology Publications, 2011. http://dx.doi.org/10.5220/0003557802010206.

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AL-Rammahi, Ali Hussain, and Tahir R. Dikheel. "Freund’s model with iterated sure independence screening in Cox proportional hazard model." In PROCEEDING OF THE 1ST INTERNATIONAL CONFERENCE ON ADVANCED RESEARCH IN PURE AND APPLIED SCIENCE (ICARPAS2021): Third Annual Conference of Al-Muthanna University/College of Science. AIP Publishing, 2022. http://dx.doi.org/10.1063/5.0093464.

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Shen, Yu, Yong Yang, Bin Ji, Zeyang Tang, Fan Yang, and Lei Wan. "Influence Factors Analysis of Distribution Transformer Fault Using Cox Proportional Hazard Model." In 2019 International Conference on Intelligent Computing, Automation and Systems (ICICAS). IEEE, 2019. http://dx.doi.org/10.1109/icicas48597.2019.00091.

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Grzyb, Megan, Amber Zhang, Cristina Good, Khaled Khalil, Bochen Guo, Lu Tian, Jose Valdez, and Quanquan Gu. "Multi-task cox proportional hazard model for predicting risk of unplanned hospital readmission." In 2017 Systems and Information Engineering Design Symposium (SIEDS). IEEE, 2017. http://dx.doi.org/10.1109/sieds.2017.7937729.

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Arora, Sanvi, Aman Kumar, and Saurabh Sambhav. "Analysing the Effect of Gender on Mortality of COVID-19 Patients through Cox-Proportional Hazard Model." In 2021 International Conference on Intelligent Technologies (CONIT). IEEE, 2021. http://dx.doi.org/10.1109/conit51480.2021.9498331.

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Deng, Xiao-Lan, and Ting Wang. "Stock Market Factors and Risk of Financial Distress: An Empirical Analysis Using Cox proportional Hazard Model." In 2008 4th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM). IEEE, 2008. http://dx.doi.org/10.1109/wicom.2008.2420.

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Xue, Zongqi, Zhenglin Liang, Minyuan Song, Chunhui Guo, and Junqi Zeng. "Base station network alarm streams modeling and prediction based on Cox proportional hazard model and copula." In 2021 IEEE 21st International Conference on Software Quality, Reliability and Security Companion (QRS-C). IEEE, 2021. http://dx.doi.org/10.1109/qrs-c55045.2021.00117.

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Alsaedi, Abdalrahman, Ikhlas Abdel-Qader, Niaz Mohammad, and Alvis C. Fong. "Extended cox proportional hazard model to analyze and predict conversion from mild cognitive impairment to alzheimer's disease." In 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC). IEEE, 2018. http://dx.doi.org/10.1109/ccwc.2018.8301669.

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Farhangdoost, Khalil, and Mehran Siahpoosh. "On the Fatigue Life Prediction of Die-Marked Drillpipes." In ASME 2006 Pressure Vessels and Piping/ICPVT-11 Conference. ASMEDC, 2006. http://dx.doi.org/10.1115/pvp2006-icpvt-11-93181.

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Drillpipe fatigue damage occurs under cyclic loading conditions due to, for instance, rotation in a dogleg region. Usually, failure mechanisms develop in the transition region of the tool joints and the die-marks due to gripping systems intensify it. In this paper two approaches are presented to evaluate damage in drillpipes; FEM and Cox Regression Model. First, Finite Element Method is used to evaluate cumulative effects of fatigue damage in a number of drilling events with respect to rotating cyclic bending and constant tension and internal pressure in a G-105 drillpipe. The results show that how die-marks or other surface crushes can reduce the fatigue life of the pipe. The presented graphs can be easily used to determine the allowable length of a G-105 drillpipe below dogleg that consumes the fatigue life of the pipe section. In the second approach, as a case study, the Cox Regression Model, a broadly applicable and the most widely used method of survival analysis is used to evaluate the distribution of survival times for the failure data of the southern oilfields of Iran. The resultant cumulative survival and hazard functions can reliably predict the time of failure and assist the engineers to evaluate cumulative damage.
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Garcez, Flávia Barreto, Wilson Jacob-Filho, and Thiago Junqueira Avelino-Silva. "EFFECT OF AMBIENT TEMPERATURE ON MORTALITY IN ACUTELY ILL HOSPITALIZED OLDER PATIENTS." In XXII Congresso Brasileiro de Geriatria e Gerontologia. Zeppelini Publishers, 2021. http://dx.doi.org/10.5327/z2447-21232021res03.

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OBJECTIVE: To investigate the association between extremes of temperature and increased hospital mortality in acutely ill older patients. METHODS: A prospective cohort study of acutely ill patients aged 60 years or older, admitted to the geriatric ward of Hospital das Clinicas at the University of Sao Paulo Medical School, from 2009 to 2015. Meteorological data were obtained through the System of Information on Air Quality of the state of Sao Paulo. The average daily temperatures were categorized according to percentiles (p). Temperatures at p95 and p90 were defined as extreme heat and those below p10 and p5 as extreme cold. We collected sociodemographic, clinical, functional, and laboratory data on admission using a standardized comprehensive geriatric assessment. The primary outcome was hospital mortality. We performed multivariate analyses using Cox proportional hazards model adjusted for confounders. RESULTS: We included 1403 patients, with a mean age of 80 years; 61% were women. The overall mortality was 19%. Temperature cutoffs by percentile were 15, 16, 25, and 26°C. The adjusted hazard ratio for all-cause mortality in the ≥ 26°C temperature group compared to the 16.1–25.0°C group was 1.89 (27 vs 18%; 95%CI 1.14–3.12; p = 0.013). There was no significant association between the other temperature groups and mortality. CONCLUSIONS: A daily temperature > 26°C was independently associated with increased hospital mortality. Health administrators and clinicians should be aware of the potential negative effects of high ambient temperatures on hospitalized older patients.
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Reports on the topic "Cox Proportional Hazard Regression Model"

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Mirel, Lisa, Cindy Zhang, Christine Cox, Ye Yeats, Félix Suad El Burai, and Golden Cordell. Comparative analysis of the National Health and Nutrition Examination Survey public-use and restricted-use linked mortality files. Centers for Disease Control and Prevention (U.S.), May 2021. http://dx.doi.org/10.15620/cdc:104744.

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"Objectives—Linking national survey data with administrative data sources enables researchers to conduct analyses that would not be possible with each data source alone. Recently, the Data Linkage Program at the National Center for Health Statistics (NCHS) released updated Linked Mortality Files, including the National Health and Nutrition Examination Survey data linked to the National Death Index mortality files. Two versions of the files were released: restricted-use files available through NCHS and Federal Statistical Research Data Centers and public-use files. To reduce the reidentification risk, statistical disclosure limitation methods were applied to the public-use files before they were released. This included limiting the amount of mortality information available and perturbing cause of death and follow-up time for select records. Methods—To assess the comparability of the restricted-use and public-use files, relative hazard ratios for all-cause and cause-specific mortality using Cox proportional hazards models were estimated and compared. Results—The comparative analysis found that the two data files yield similar descriptive and model results. Suggested citation: Mirel LB, Zhang C, Cox CS, Ye Y, El Burai Félix S, Golden C. Comparative analysis of the National Health and Nutrition Examination Survey public-use and restricted-use linked mortality files. National Health Statistics Reports; no 155. Hyattsville, MD: National Center for Health Statistics. 2021. DOI: https://doi.org/10.15620/cdc:104744. CS323656 nhsr155-508.pdf"
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