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

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Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

HUDORI, MAHFUZ. "FAKTOR-FAKTOR YANG MEMPENGARUHI DAYA TAHAN MAHASISWA TEKNIK SIPIL UIB DALAM MEMPERTAHANKAN STUDINYA." E-Jurnal Matematika 10, no. 1 (January 31, 2021): 1. http://dx.doi.org/10.24843/mtk.2021.v10.i01.p312.

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Linear regression model cannot be used to analyze the relationship between survival time and independent variables, it is because the linear regression model is not able to handle censored data. Regression can be used to analyze survival data is cox proportional hazard regression. This research studies factors that influence study time drop out of Civil Engineering students at Universitas International Batam using the cox proportional hazard regression model approach. The independent variable that influenced study time drop out of Civil Engineering students at Universitas International Batam was the Cumulative Achievement Index and Work Status.
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12

Chernova, Oksana, and Alexander Kukush. "Testing Linear and Nonlinear Hypotheses in a Cox Proportional Hazards Model with Errors in Covariates." Lietuvos statistikos darbai 58, no. 1 (December 20, 2019): 39–47. http://dx.doi.org/10.15388/ljs.2019.16669.

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We investigate linear and nonlinear hypotheses testing in a Cox proportional hazards model for right-censored survival data when the covariates are subject to measurement errors. In Kukush and Chernova (2018) [Theor. Probability and Math. Statist. 96, 101–110], a consistent simultaneous estimator is introduced for the baseline hazard rate and the vector of regression parameters. Therein the baseline hazard rate belongs to an unbounded set of nonnegative Lipschitz functions, with fixed constant, and the vector of regression parameters belongs to a compact parameter set. Based on the estimator, we develop two procedures to test nonlinear and linear hypotheses about the vector of regression parameters: Wald-type and score-type tests. The latter is based on an unbiased estimating equation. The consistency of the tests is shown.
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PUSPITA, NI NENGAH RIKA, MADE SUSILAWATI, and NI LUH PUTU SUCIPTAWATI. "APLIKASI COX PROPORTIONAL HAZARD PADA SINTASAN PASIEN ASMA." E-Jurnal Matematika 11, no. 1 (January 31, 2022): 53. http://dx.doi.org/10.24843/mtk.2022.v11.i01.p360.

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Asthma is defined as a chronic inflammatory disease of the respiratory tract. Since 2013, Bali ranks sixth out of thirty three provinces in Indonesia for the most asthma patient. This study has a purpose to examine the influential factors on the cure rate of asthma patients and determine the best model using the stepwise method. To determine the survival rate of asthma patients, a statistical method that involves censored data is used by applying the Cox Proportional Hazard regression. The data used in this study were medical records of asthma patients who were hospitalized at the Wongaya Regional General Hospital in Denpasar for the period January , 2019 to April , 2020. The analysis of this study discovered that significant variables for the survival of asthma patients were age and disease.
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Mahmudah, Umi, Sugiyarto Surono, Puguh Wahyu Prasetyo, Muhamad Safiih Lola, and Annisa Eka Haryati. "COX PROPORTIONAL HAZARD REGRESSION SURVIVAL ANALYSIS FOR TYPE 2 DIABETES MELITUS." BAREKENG: Jurnal Ilmu Matematika dan Terapan 16, no. 1 (March 21, 2022): 253–62. http://dx.doi.org/10.30598/barekengvol16iss1pp251-260.

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One of the most widely used methods of survival analysis is Cox proportional hazard regression. It is a semiparametric regression used to investigate the effects of a number of variables on the dependent variable based on survival time. Using the Cox proportional hazard regression method, this study aims to estimate the factors that influence the survival of patients with type 2 diabetes mellitus. The estimated parameter values, as well as the Cox Regression equation model, were also investigated. A total of 1293 diabetic patients with type 2 diabetes were studied, with data taken from medical records at PKU Muhammadiyah Hospital in Yogyakarta, Indonesia. These variables have regression coefficients of 1.36, 1.59, -0.63, 0.11, and 0.51, respectively. Furthermore, the results showed the hazard ratio for female patients was 1.16 times male patients. Patients on insulin treatment had a 4.92-fold higher risk of death than those on other therapy profiles. Patients with normal blood sugar levels (GDS 140 mg/dl) had a 1.12 times higher risk of death than those with other blood glucose levels. Type 2 diabetes mellitus is a challenge for many Indonesians, in addition to being a deadly condition that was initially difficult to diagnose. As a result, patient survival analysis is needed to reduce the patient's risk of death.
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Kong, Fan Hui. "Adjusting regression attenuation in the Cox proportional hazards model." Journal of Statistical Planning and Inference 79, no. 1 (June 1999): 31–44. http://dx.doi.org/10.1016/s0378-3758(98)00178-5.

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Mauger, Elizabeth A., Robert A. Wolfe, and Friedrich K. Port. "Transient effects in the cox proportional hazards regression model." Statistics in Medicine 14, no. 14 (July 30, 1995): 1553–65. http://dx.doi.org/10.1002/sim.4780141406.

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Chandra, Novita Eka, and Siti Alfiatur Rohmaniah. "ANALISIS SURVIVAL MODEL REGRESI SEMIPARAMETRIK PADA LAMA STUDI MAHASISWA." Jurnal Ilmiah Teknosains 5, no. 2 (February 3, 2020): 94. http://dx.doi.org/10.26877/jitek.v5i2.4256.

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In survival analysis to determine the relationship between variables is used a regression model, one of which uses the semiparametric regression model. The semiparametric regression model is a model that does not require assumptions or information on survival data distribution. That way, this model is more flexible in its use. In this study, the semiparametric regression model used the Cox Proportional Hazard (Cox PH) regression model. Estimation of Cox PH regression parameters can be done without determining the function baseline hazard. The purpose of this study is to determine the factors that influence the duration of student studies. If there are many students whose studies have not been on time, it shows that there is a lack of professionalism in the academic field of the educator. Thus, the community will assess the low quality of the university, resulting in a decrease in the number of students who want to study at the university. The samples in this study were students of class 2014 Universitas Islam Darul Ulum Lamongan. The variables have used the length of study for students, gender, GPA, school origin, organization, and work. Based on the results of the assumption Proportional Hazard (PH) conducted, all independent variables have fulfilled these assumptions, so that these variables can be used in Cox PH regression. After parameter estimation by Cox PH regression, the GPA and organizational factors significantly influence the duration of student study. Students with high GPA and participating in organizations more quickly complete their studies.
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İnan, Deniz, and Müjgan Tez. "A New Estimator for Cox Proportional Hazard Regression Model in Presence of Collinearity." Communications in Statistics - Theory and Methods 41, no. 13-14 (July 2012): 2437–44. http://dx.doi.org/10.1080/03610926.2012.659827.

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Hess, Wolfgang, Gerhard Tutz, and Jan Gertheiss. "A Flexible Link Function for Discrete-Time Duration Models." Jahrbücher für Nationalökonomie und Statistik 236, no. 4 (August 1, 2016): 455–81. http://dx.doi.org/10.1515/jbnst-2015-1022.

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Abstract This paper proposes a discrete-time hazard regression approach based on the relation between hazard rate models and excess over threshold models, which are frequently encountered in extreme value modelling. The proposed duration model employs a flexible link function and incorporates the grouped-duration analogue of the well-known Cox proportional hazards model and the proportional odds model as special cases. The theoretical setup of the model is motivated, and simulation results are reported, suggesting that the model proposed performs well. The simulation results and an empirical analysis of US import durations also show that the choice of link function in discrete hazard models has important implications for the estimation results, and that severe biases in the results can be avoided when using a flexible link function.
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Afni, Yusrillah Ihza Zianita, Mohamad Fatekurohman, and Dian Anggraeni. "Perbandingan Model Cox Proportional Hazard dan Regresi Weibull untuk Menganalisis Ketahanan Bank Syariah." Indonesian Journal of Applied Statistics 2, no. 2 (December 27, 2019): 127. http://dx.doi.org/10.13057/ijas.v2i2.33082.

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<p>On July 1, 2014 Financial Services Authority (OJK) issued a new regulation number 8/PJOK.13/2014 concerning the health of general sharia banks that can be valued from several aspects including credit risk, liquidity risk, Return on Asset (ROA), Net of Margin (NOM) and Capital Adequacy Ratio (CAR). The purpose of this study is to compare the models of Cox proportional hazard and Weibull regression for the resistance of sharia bank in 2017-2018 for 24 data. The data were analyzed by describing each variable and modeling in each method. Comparison result shows that Weibull regression model is better than the Cox proportional hazard model because it has smaller AIC and MSE.</p><p><strong>Keywords : </strong>Sharia Bank, Survival Analysis, AIC, MSE</p>
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Arifitriana, Wirna, and Danardono Danardono. "PENERAPAN MODEL PENYEMBUHAN DENGAN REGRESI COX HAZARD PROPORSIONAL PADA PENYAKIT KANKER KOLOREKTAL." EKSAKTA : Jurnal Penelitian dan Pembelajaran MIPA 4, no. 1 (January 27, 2019): 66. http://dx.doi.org/10.31604/eksakta.v4i1.66-72.

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Survival analysis is a statistical technique used to analyze the data, aims to determine the variables that affect the outcome of a beginning to end the incident. One model of survival is a cure model is useful for estimating the proportion of patients who recover and the probability of survival of patients who did not recover until the deadline given. Analysis on Cox regression cure model Hazard Proportional with Maximum Likelihood Estimates and Algorithm Expectation Maximization (EM). Keywords: Cox Proportional Hazard Cure Model, MLE, EM algorithm, likelihood ratio test, Wald test.
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Setiani, Eri, Sudarno Sudarno, and Rukun Santoso. "PERBANDINGAN MODEL REGRESI COX PROPORTIONAL HAZARD MENGGUNAKAN METODE BRESLOW DAN EFRON (Studi Kasus: Penderita Stroke di RSUD Tugurejo Kota Semarang)." Jurnal Gaussian 8, no. 1 (February 28, 2019): 93–105. http://dx.doi.org/10.14710/j.gauss.v8i1.26624.

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Cox proportional hazard regression is a regression model that is often used in survival analysis. Survival analysis is phrase used to describe analysis of data in the form of times from a well-defined time origin until occurrence of some particular even or end-point. In analysis survival sometimes ties are found, namely there are two or more individual that have together event. This study aims to apply Cox model on ties event using two methods, Breslow and Efron and determine factors that affect survival of stroke patients in Tugurejo Hospital Semarang. Dependent variable in this study is length of stay, then independent variables are gender, age, type of stroke, history of hypertension, systolic blood pressure, diastolic blood pressure, blood sugar levels, and BMI. The two methods give different result, Breslow has four significant variables there are type of stroke, history of hypertension, systolic blood pressure, and diastolic blood pressure, while Efron contains five significant variables such as type of stroke, history of hypertension, systolic blood pressure, diastolic blood pressure and blood sugar levels. From the smallest AIC criteria obtained the best Cox proportional hazard regression model is Efron method. Keywords: Stroke, Cox Proportional Hazard Regression model, Breslow method, Efron method.
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Farooq, Fabiha Binte, and Md Jamil Hasan Karami. "Model Selection Strategy for Cox Proportional Hazards Model." Dhaka University Journal of Science 67, no. 2 (July 30, 2019): 111–16. http://dx.doi.org/10.3329/dujs.v67i2.54582.

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Often in survival regression modelling, not all predictors are relevant to the outcome variable. Discarding such irrelevant variables is very crucial in model selection. In this research, under Cox Proportional Hazards (PH) model we study different model selection criteria including Stepwise selection, Least Absolute Shrinkage and Selection Operator (LASSO), Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and the extended versions of AIC and BIC to the Cox model. The simulation study shows that varying censoring proportions and correlation coefficients among the covariates have great impact on the performances of the criteria to identify a true model. In the presence of high correlation among the covariates, the success rate for identifying the true model is higher for LASSO compared to other criteria. The extended version of BIC always shows better result than the traditional BIC. We have also applied these techniques to real world data. Dhaka Univ. J. Sci. 67(2): 111-116, 2019 (July)
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Li, Kexuan. "Variable Selection for Nonlinear Cox Regression Model via Deep Learning." International Journal of Statistics and Probability 12, no. 1 (December 24, 2022): 21. http://dx.doi.org/10.5539/ijsp.v12n1p21.

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Variable selection problem for the nonlinear Cox regression model is considered. In survival analysis, one main objective is to identify the covariates that are associated with the risk of experiencing the event of interest. The Cox proportional hazard model is being used extensively in survival analysis in studying the relationship between survival times and covariates, where the model assumes that the covariate has a log-linear effect on the hazard function. However, this linearity assumption may not be satisfied in practice. In order to extract a representative subset of features, various variable selection approaches have been proposed for survival data under the linear Cox model. However, there exists little literature on variable selection for the nonlinear Cox model. To break this gap, we extend the recently developed deep learning-based variable selection model LassoNet to survival data. Simulations are provided to demonstrate the validity and effectiveness of the proposed method. Finally, we apply the proposed methodology to analyze a real data set on diffuse large B-cell lymphoma.
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Yin, Zhiying, Canjie Zheng, Quanjun Fang, Xiaoying Gong, Guoping Cao, Junji Li, Ziling Xiang, and Wei Song. "Introduction of Two-Dose Mumps-Containing Vaccine into Routine Immunization Schedule in Quzhou, China, Using Cox-Proportional Hazard Model." Journal of Immunology Research 2021 (November 5, 2021): 1–8. http://dx.doi.org/10.1155/2021/5990417.

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Mumps is a vaccine-preventable disease caused by the mumps virus, but the incidence of mumps has increased among the children who were vaccinated with one-dose measles-mumps-rubella (MMR) in recent years. In this study, we analyzed the influence of different doses of mumps-containing vaccine (MuCV) against mumps using Cox-proportional hazard model. We collected 909 mumps cases of children who were born from 2006 to 2010 and vaccinated with different doses of MuCV in Quzhou during 2006-2018, which were all clinically diagnosed. Kaplan-Meier survival methods and Cox-proportional hazard model were used to estimate the hazard probabilities. Kaplan–Meier curves showed that the cumulative hazard of male and female has no difference; lower hazards were detected among those who were vaccinated with two-dose MuCV, born in 2006, and infected after supplementary immunization activities (SIA). Cox-proportional hazard regression suggested that onset after SIA, born in 2006, and vaccinated with two-dose MuCV were protective factors against infection even after adjusting for potential confounding effects. Our study showed that it was necessary to revise the diagnostic criteria of mumps and identify RT-PCR as the standard for mumps diagnosis in China. We suggested that routine immunization schedule should introduce two doses of MMR and prevaccination screening should be performed before booster immunization in vaccinated populations.
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Fisher, Lloyd D., and D. Y. Lin. "TIME-DEPENDENT COVARIATES IN THE COX PROPORTIONAL-HAZARDS REGRESSION MODEL." Annual Review of Public Health 20, no. 1 (May 1999): 145–57. http://dx.doi.org/10.1146/annurev.publhealth.20.1.145.

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Lee, Jong Gun, Sue Moon, and Kavé Salamatian. "Modeling and predicting the popularity of online contents with Cox proportional hazard regression model." Neurocomputing 76, no. 1 (January 2012): 134–45. http://dx.doi.org/10.1016/j.neucom.2011.04.040.

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Forestryani, Veniola, Mohamad Fatekurohman, and Alfian Futuhul Hadi. "Survival Analysis of Sea Turtles Eggs Hatching Success using Cox non Proportional Hazard Regression." Jurnal ILMU DASAR 20, no. 1 (January 22, 2019): 19. http://dx.doi.org/10.19184/jid.v20i1.6531.

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The aims of this research is to know both the model and also the factors of incubation period and hatching success of eggs of sea turtles in Kuta, Legian and Seminyak Beach, Bali from January to September 2016. The reasearch was conducted by doing survival analysis by using Cox Non Proportional Hazard regression and then compare the model derived from it with log-logistic regression model. Precipitation, location, temperature, humidity, and hours of daylight are the factors which significantly influence incubation period and hatching success of eggs of sea turtles. According to the descriptive analysis, 12≤ precipitaion <18, Seminyak Beach, 28,5≤ temperature <29,5, 86≤ humidity ≤91, and 5,8≤ hours of daylight <8,3 are the factors which have highest percentage of hatching success. Meanwhile 12≤ precipitation <18, Seminyak Beach, 28,5≤ temperature <29,5, 86≤ humidity ≤91, and 0,8≤ hours of daylight <3,3 are the factors which have highest percentage of hatching success based on the hazard value. Although Seminyak Beach has the highest rate of hatching success, it’s not significantly different from Legian beach in respect to the location factor’s categories. Keywords: hatching success, cox non proportional hazard, log-logistic, survival analysis
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Akbas, Kubra, Ipek Cicek, Mehmet Kaya, and Cemil Colak. "Comparison of Performance of Deep Survival and Cox Proportional Hazard Models: an Application on the Lung Cancer Dataset." Medicine Science | International Medical Journal 11, no. 3 (2022): 1202. http://dx.doi.org/10.5455/medscience.2022.03.078.

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The goal of this study is to compare the performance of the deep survival model and the Cox regression model in an open-access Lung cancer dataset consisting of survivors and dead patients. In the study, it is applied to an open access dataset named "Lung Cancer Data" to compare the performances of the CPH and deepsurv models. The performance of the models is evaluated by C-index, AUC, and Brier score. The concordance index of the deep survival model is 0.64296, the Brier score was 0.128921, and the AUC was 0.6835. With the Cox regression model, the concordance index is calculated as 0.61445, brier score 0.1667, and AUC 0.5832. According to the Concordance index, brier score, and AUC criteria, the deep survival model performed better than the cox regression model. DeepSurv's forecasting, modeling, and predictive capabilities pave the path for future deep neural network and survival analysis research. DeepSurv has the potential to supplement traditional survival analysis methods and become the standard method for medical doctors to examine and offer individualized treatment alternatives with more research.
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Sudarno, Sudarno, and Eri Setiani. "HAZARD PROPORTIONAL REGRESSION STUDY TO DETERMINE STROKE RISK FACTORS USING BRESLOW METHOD." MEDIA STATISTIKA 12, no. 2 (December 30, 2019): 200. http://dx.doi.org/10.14710/medstat.12.2.200-213.

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Cox proportional hazard regression is a regression model that is often used in survival analysis. Survival analysis is phrase used to describe analysis of data in the form of times from a well-defined time origin until occurrence of some particular be death. In analysis survival sometimes ties are found, namely there are two or more individual that have together event. The objectives of this research are applied Cox proportional hazard regression on ties event using Breslow methodand determine factors that affect survival of stroke patients in Tugurejo Hospital Semarang. The response variable is length of stay at hospital, and the predictors are gender, age, type of stroke, history of hypertension, systolic blood pressure, diastolic blood pressure, blood sugar levels, and body mass index. The factors cause stroke disease by significant are type of stroke, history of hypertension, systolic blood pressure, diastolic blood pressure, and blood sugar level. By the survivorship function that the patients have been looked after at hospital greater than 20 days, they have probability of healthy be little even go to death. A person in order to be healthy must notice and prevent some factors cause disease.
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Babińska, Magdalena, Jerzy Chudek, Elżbieta Chełmecka, Małgorzata Janik, Katarzyna Klimek, and Aleksander Owczarek. "Limitations of Cox Proportional Hazards Analysis in Mortality Prediction of Patients with Acute Coronary Syndrome." Studies in Logic, Grammar and Rhetoric 43, no. 1 (December 1, 2015): 33–48. http://dx.doi.org/10.1515/slgr-2015-0040.

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Abstract The aim of this study was to evaluate the possibility of incorrect assessment of mortality risk factors in a group of patients affected by acute coronary syndrome, due to the lack of hazard proportionality in the Cox regression model. One hundred and fifty consecutive patients with acute coronary syndrome (ACS) and no age limit were enrolled. Univariable and multivariable Cox proportional hazard analyses were performed. The proportional hazard assumptions were verified using Schoenfeld residuals, χ2 test and rank correlation coefficient t between residuals and time. In the total group of 150 patients, 33 (22.0%) deaths from any cause were registered in the follow-up time period of 64 months. The non-survivors were significantly older and had increased prevalence of diabetes and erythrocyturia, longer history of coronary artery disease, higher concentrations of serum creatinine, cystatin C, uric acid, glucose, C-reactive protein (CRP), homocysteine and B-type natriuretic peptide (NT-proBNP), and lower concentrations of serum sodium. No significant differences in echocardiography parameters were observed between groups. The following factors were risk of death factors and fulfilled the proportional hazard assumption in the univariable model: smoking, occurrence of diabetes and anaemia, duration of coronary artery disease, and abnormal serum concentrations of uric acid, sodium, homocysteine, cystatin C and NT-proBNP, while in the multivariable model, the risk of death factors were: smoking and elevated concentrations of homocysteine and NT-proBNP. The study has demonstrated that violation of the proportional hazard assumption in the Cox regression model may lead to creating a false model that does not include only time-independent predictive factors.
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Nilima, Shahnaz. "Under-five child mortality in Bangladesh: Classical and Bayesian approaches to Cox proportional hazard model." Bangladesh Journal of Scientific Research 30, no. 1-2 (March 25, 2018): 45–54. http://dx.doi.org/10.3329/bjsr.v30i1-2.36119.

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This study examines the determinants of under-five child mortality in Bangladesh using the data extracted from the Bangladesh Demographic and Health Survey (BDHS), 2011 and 2014. Product-Limit method and Log-Rank test have been used for bivariate analysis. Cox proportional hazard model has been employed under both classical and Bayesian approaches. Cox regression analysis reveals that region (Barisal and Sylhet), maternal education (higher education), mother’s membership of NGO have significant impact on child mortality. The results obtained using Bayesian Cox PH model are almost similar except one key finding. Under Bayesian analysis, child’s size at birth appeared as potential determinant of under-five mortality whereas it has insignificant effect on child survival when classical Cox model has been applied.Bangladesh J. Sci. Res. 30(1&2): 45-54, December-2017
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Mohammadi, Golshan, Reza Tavakkoli-Moghaddam, and Mehrdad Mohammadi. "Hierarchical Neural Regression Models for Customer Churn Prediction." Journal of Engineering 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/543940.

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As customers are the main assets of each industry, customer churn prediction is becoming a major task for companies to remain in competition with competitors. In the literature, the better applicability and efficiency of hierarchical data mining techniques has been reported. This paper considers three hierarchical models by combining four different data mining techniques for churn prediction, which are backpropagation artificial neural networks (ANN), self-organizing maps (SOM), alpha-cut fuzzyc-means (α-FCM), and Cox proportional hazards regression model. The hierarchical models are ANN + ANN + Cox, SOM + ANN + Cox, andα-FCM + ANN + Cox. In particular, the first component of the models aims to cluster data in two churner and nonchurner groups and also filter out unrepresentative data or outliers. Then, the clustered data as the outputs are used to assign customers to churner and nonchurner groups by the second technique. Finally, the correctly classified data are used to create Cox proportional hazards model. To evaluate the performance of the hierarchical models, an Iranian mobile dataset is considered. The experimental results show that the hierarchical models outperform the single Cox regression baseline model in terms of prediction accuracy, Types I and II errors, RMSE, and MAD metrics. In addition, theα-FCM + ANN + Cox model significantly performs better than the two other hierarchical models.
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Qomaria, Tutik, Mohamad Fatekurohman, and Dian Anggraeni. "Aplikasi Model Cox Proportional Hazard pada Pasien Stroke RSD Balung Kabupaten Jember." Indonesian Journal of Applied Statistics 2, no. 2 (December 27, 2019): 94. http://dx.doi.org/10.13057/ijas.v2i2.34907.

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<p>According to the World Health Organization (WHO) cardiovascular disease is a disease caused by impaired heart and blood vessel function. There are many types of cardiovascular disease, but the most common and most well-known are coronary heart disease and stroke. Stroke is a syndrome characterized by symptoms and / or rapidly developing clinical signs in the form of focal and global brain functional disorders lasting more than 24 hours (unless there are surgical interventions or bringing death), which are not caused by other causes besides vascular causes. The number of stroke patients in Indonesia in 2013 based on the diagnosis of health personnel (Nakes) was 1.236.825 (7,0%), while based on the diagnosis of symptoms was 2.137.941 (12,1%). In this study the factors that can affect the survival of stroke sufferers were analyzed using the Cox proportional hazard regression model, the dependent variable was the length of time the patient was treated and the independent variables were gender, age, hypertension status, cholesterol status, Diabetes Militus (DM) status, stroke type, and Body Mass Index (BMI). The result showed that age, DM status, and type of stroke were the most influential factors on the survival of stroke patients at Balung Regional Hospital.</p><strong>Keywords : </strong>stroke disease, survival analysis, Cox proportional hazard model
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Devarajan, Karthik, and Nader Ebrahimi. "Testing for Covariate Effect in the Cox Proportional Hazards Regression Model." Communications in Statistics - Theory and Methods 38, no. 14 (July 6, 2009): 2333–47. http://dx.doi.org/10.1080/03610920802536958.

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Silmi, Asri Lutfia, Sudarno Sudarno, and Puspita Kartikasari. "PERBANDINGAN MODEL REGRESI KEGAGALAN PROPORSIONAL DARI COX MENGGUNAKAN METODE EFRON DAN EXACT." Jurnal Gaussian 9, no. 4 (December 7, 2020): 474–85. http://dx.doi.org/10.14710/j.gauss.v9i4.29008.

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Cox proportional hazard regression analysis is one of statistical methods that is often used in survival analysis to determine the effect of independent variables on the dependent variable in the form of survival time. Survival time starts from the beginning of the study until the event occurs or has reached the end of the study. The Cox proportional hazard regression model does not require information about the distribution that underlies the survival time but there is an assumption of proportional hazard that must be met. The purpose of this study is to determine the factors that influence the survival time of coronary heart disease. Ties are often found in survival data, including the survival data used in this study. Ties is an event when there are two or more individuals who experience a failure at the same time or have the same survival time value. The Efron and Exact method approach is used to overcome the presence of ties that can cause problems in the estimation of parameters associated with determining the members of the risk set. The results showed that the variables of diabetes mellitus, family history, and platelets significantly affected the survival time of CHD patients for both methods. The best model obtained is the Exact method because it has smaller AIC value of 383,153 compared to the AIC value of the Efron method of 393,207.
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Antika, Dwi Putri, Mohamat Fatekurohman, and I. Made Tirta. "Banking Credit Risk Analysis with Naive Bayes Approach and Cox Proportional Hazard." International Journal of Advanced Engineering Research and Science 9, no. 8 (2022): 365–70. http://dx.doi.org/10.22161/ijaers.98.41.

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Credit is needed for some people for certain purposes. In credit, it takes a party that can be used as an intermediary such as a bank. The debtor may not be able to make payments according to the original policy or even cause losses where the Bank may lose the opportunity to earn interest, causing a decrease in total income. This problem is included in the case of non-performing loans. In statistics, the duration of time between a person not making a payment on time until a non-current loan occurs can be predicted using survival analysis. Meanwhile, to predict credit status, you can use classification or prediction methods in machine learning to find out how much influence the predictor variable has. In this study, with a different case, focusing on the credit risk case of how a bank decides to provide credit to prospective debtors using the classifier method found in Machine Learning, namely Naive Bayes and Cox regression from survival analysis. Through the evaluation test of the naive bayes classifier model using accuracy values, confusion matrix and ROC, it can be concluded that this model is a model with good performance for predicting credit status. Multinomial nave Bayes in this study has a higher performance value than Gaussian Naïve Bayes and Bernoulli Naïve Bayes which is 92%. Through cox regression, it is obtained that income factors and loan history have a major influence on determining credit status.
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Journal, Baghdad Science. "Cox proportion hazard model for patients with hepatitis disease in Iraq." Baghdad Science Journal 6, no. 3 (September 6, 2009): 612–17. http://dx.doi.org/10.21123/bsj.6.3.612-617.

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Cox regression model have been used to estimate proportion hazard model for patients with hepatitis disease recorded in Gastrointestinal and Hepatic diseases Hospital in Iraq for (2002 -2005). Data consists of (age, gender, survival time terminal stat). A Kaplan-Meier method has been applied to estimate survival function and hazerd function.
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Isam Alskal, Oday, and Zakariya Yahya Algamal. "Gene selection in Cox regression model based on a new adaptive penalized method." International Journal of Advanced Statistics and Probability 8, no. 1 (May 15, 2020): 16. http://dx.doi.org/10.14419/ijasp.v8i1.30566.

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The common issues of high dimensional gene expression data for survival analysis are that many of genes may not be relevant to their diseases. Gene selection has been proved to be an effective way to improve the result of many methods. The Cox proportional hazards regression model is the most popular model in regression analysis for censored survival data. In this paper, an adaptive penalized Cox proportional hazards regression model is proposed, with the aim of identification relevant genes and provides high classification accuracy, by combining the Cox proportional hazards regression model with the weighted least absolute shrinkage and selection operator (LASSO) method. Experimental results show that the proposed method significantly outperforms two competitor methods in terms of the area under the curve and the number of the selected genes.
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Abd ElHafeez, Samar, Graziella D’Arrigo, Daniela Leonardis, Maria Fusaro, Giovanni Tripepi, and Stefanos Roumeliotis. "Methods to Analyze Time-to-Event Data: The Cox Regression Analysis." Oxidative Medicine and Cellular Longevity 2021 (November 30, 2021): 1–6. http://dx.doi.org/10.1155/2021/1302811.

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The Cox model is a regression technique for performing survival analyses in epidemiological and clinical research. This model estimates the hazard ratio (HR) of a given endpoint associated with a specific risk factor, which can be either a continuous variable like age and C-reactive protein level or a categorical variable like gender and diabetes mellitus. When the risk factor is a continuous variable, the Cox model provides the HR of the study endpoint associated with a predefined unit of increase in the independent variable (e.g., for every 1-year increase in age, 2 mg/L increase in C-reactive protein). A fundamental assumption underlying the application of the Cox model is proportional hazards; in other words, the effects of different variables on survival are constant over time and additive over a particular scale. The Cox regression model, when applied to etiological studies, also allows an adjustment for potential confounders; in an exposure-outcome pathway, a confounder is a variable which is associated with the exposure, is not an effect of the exposure, does not lie in the causal pathway between the exposure and the outcome, and represents a risk factor for the outcome.
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Kung, Yung-Jen, Chang-Ching Wei, Liuh An Chen, Jiin Yi Chen, Ching-Yao Chang, Chao-Jen Lin, Yun-Ping Lim, et al. "Kawasaki Disease Increases the Incidence of Myopia." BioMed Research International 2017 (2017): 1–6. http://dx.doi.org/10.1155/2017/2657913.

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The prevalence of myopia has rapidly increased in recent decades and has led to a considerable global public health concern. In this study, we elucidate the relationship between Kawasaki disease (KD) and the incidence of myopia. We used Taiwan’s National Health Insurance Research Database to conduct a population-based cohort study. We identified patients diagnosed with KD and individuals without KD who were selected by frequency matched based on sex, age, and the index year. The Cox proportional hazards regression model was used to estimate the hazard ratio and 95% confidence intervals for the comparison of the 2 cohorts. The log-rank test was used to test the incidence of myopia in the 2 cohorts. A total of 532 patients were included in the KD cohort and 2128 in the non-KD cohort. The risk of myopia (hazard ratio, 1.31; 95% confidence interval, 1.08–1.58; P<0.01) was higher among patients with KD than among those in the non-KD cohort. The Cox proportional hazards regression model showed that irrespective of age, gender, and urbanization, Kawasaki disease was an independent risk factor for myopia. Patients with Kawasaki disease exhibited a substantially higher risk for developing myopia.
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42

PARFENTSEVA, N., and H. HOLUBOVA. "Simulating Financial Risks on the Basis of Statistical Assessment Methods." Scientific Bulletin of the National Academy of Statistics, Accounting and Audit, no. 1-2 (June 1, 2022): 14–20. http://dx.doi.org/10.31767/nasoa.1-2-2022.02.

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Financial risks are summed up by type and form, macro- and micro-level. The importance of monitoring of financial risks in insurance, banking, monetary activities and various business processes is substantiated. It is emphasized that the sound assessment of risks constitutes an important tool in risk management. The essence, advantages and disadvantages of selected methods for financial risk assessment is shown: Value-at-Risk, Monte Carlo method, methods based on IRB approach, Shortfall, LDA, methods using Bayesian programming. The importance of statistical methods for the assessment of financial risks like non-parametric techniques of Kaplan – Meier and Cox proportional hazards model is substantiated. It is emphasized that the cumulative hazard function by Cox model reflects the cumulative level of bank losses, hence, its application in risk assessment is capable to protect and warn the bank about a potential threat. Kaplan – Meier method allows to assess the probability of risk occurrence and risk level in various client groups, which is a necessary component of risk monitoring. But its drawback is its incapability to account for several risks at the same time. In view of this, a sounder method for risk assessment is Cox proportional hazard regression. The input data for constructing this regression can include both categorical and continuous variables, thus enabling for accounting of a multiplicity of risk-related factors. It is concluded that Kaplan – Meier method should be used with caution, because the survival function may overvalue the probability of occurrence of “critical” event, depending on the internal nature of data and their individual variances. Hence, applications of semiparametric techniques of Cox proportional hazards model should be an alternative approach to the survival analysis.
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King, Richard, Jordan Schaefer, Vaibhav Sahai, Kent A. Griffith, and Suman L. Sood. "Retrospective Cohort Analysis of Aspirin Use and Venous Thromboembolism in Patients with Pancreatic Cancer and an Indwelling Central Venous Catheter." TH Open 06, no. 03 (July 2022): e221-e229. http://dx.doi.org/10.1055/s-0042-1747685.

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Abstract Background Patients with pancreatic cancer are at high risk of developing venous thromboembolism (VTE). It is unknown if aspirin reduces the risk of VTE in this setting. Objectives We sought to determine whether there is an association between aspirin use and VTE risk in patients with pancreatic cancer receiving chemotherapy with a central venous catheter (CVC). Patients/Methods We conducted a single-center, retrospective cohort study of adult patients diagnosed with pancreatic cancer and treated with chemotherapy using a CVC. Subjects were excluded if they were on anticoagulation at the time of CVC placement. The probability of VTE was analyzed using a time-to-event analysis framework for the development of VTE using the product-limit method of Kaplan and Meier (univariate) and adjusting for important confounding covariates using Cox proportional hazards regression (cause-specific hazard) and again using Fine and Gray regression (subdistributional hazard) with death prior to VTE considered a competing event. Results The final analysis included 314 cases (125 with any aspirin use and 189 without). Patients with any aspirin use had fewer VTE events (34.4%) compared with those without aspirin use (42.3%; p = 0.021) by log-rank test and after adjustment for multiple covariates using a Cox proportional hazards model (hazard ratio [HR] = 0.60; 95% confidence interval [CI]: 0.40–0.92; p = 0.019). Using Fine and Gray regression to account for death as a competing event, the effect of aspirin remained in the direction of benefit, but was not statistically significant (HR = 0.70; 95% CI: 0.47–1.05, p = 0.083). Higher body mass index, active smoking, and metastatic stage of cancer were associated with VTE events in the Cox proportional hazards model. Rates of major bleeding or clinically relevant minor bleeding were similar between treatment groups. Conclusions Aspirin may reduce the risk of VTE in patients with pancreatic cancer with a CVC. We did not observe a significant increase in the rates of major bleeding or clinically relevant nonmajor bleeding.
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Hussein, M., L. Hyman, and M. C. Leske. "Cox proportional hazard model with tied events vis-a-vis logistic regression when modeling dichotomous outcomes." Controlled Clinical Trials 19, no. 3 (June 1998): S55. http://dx.doi.org/10.1016/s0197-2456(98)80147-5.

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Hastuti, Alfiya Nurwidi, Yuciana Wilandari, and Sudarno Sudarno. "ANALISIS LAJU PERBAIKAN KONDISI KLINIS PASIEN STROKE MENGGUNAKAN REGRESI HAZARD ADITIF LIN-YING (Studi Kasus: Data Pasien Stroke di RSUD Pandan Arang Boyolali Periode Januari 2021 - Agustus 2021)." Jurnal Gaussian 11, no. 2 (August 28, 2022): 206–17. http://dx.doi.org/10.14710/j.gauss.v11i2.35465.

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Additive hazard regression is a survival analysis that is an alternative to Cox proportional hazard regression. The additive hazard models that have been developed include the Aalen additive hazard model and the Lin-Ying. In this study, Lin-Ying additive hazard regression was used as an analytical method to be applied in stroke data that had been hospitalized at Pandan Arang Hospital Boyolali. This method is considered more effective because there is no assumption of proportionality. The purpose of using this method in this study are analyze the characteristics of stroke patients, form a Lin-Ying additive hazard regression model, find out the factors that affect the rate of improvement of the clinical condition of stroke patients, and interpret the model. Based on the analysis that has been done, the average length of hospitalization is 4,471 days ≈ 4 days, and the factors that significantly affect the rate of improvement of clinical conditions in stroke patients at Pandan Arang Hospital Boyolali are blood pressure and blood sugar.
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Hafildah, Ummi, and Ria Dhea Karisma. "Analisis Ketahanan Hidup Pada Penderita Kanker Serviks Menggunakan Regresi Cox Proportional Hazard." Jurnal Riset Mahasiswa Matematika 1, no. 5 (June 29, 2022): 246–54. http://dx.doi.org/10.18860/jrmm.v1i5.14498.

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Survival analysis is a statistical method used to analyze data with time until the occurrence of a certain event which is commonly referred to as "failure". One of the objectives of survival analysis is to determine the effect of predictor variables on survival time. The purpose of this study was to determine the regression model and determine the hazard ratio of each factor that is thought to affect the survival of cervical cancer patients. The results of this study showed that the factors that influence patients with cervical cancer in their survival are stage II and stage III variables (the patient’s stage), complications, and a history of pregnancy (who have children 0-2).
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Hafildah, Ummi, and Ria Dhea Layla Nur Karisma. "Analisis Ketahanan Hidup Pada Penderita Kanker Serviks Menggunakan Regresi Cox Proportional Hazard." Jurnal Riset Mahasiswa Matematika 2, no. 2 (December 31, 2022): 59–67. http://dx.doi.org/10.18860/jrmm.v2i2.15042.

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Survival analysis is a statistical method used to analyze data with time until the occurrence of a certain event which is commonly referred to as "failure". One of the objectives of survival analysis is to determine the effect of predictor variables on survival time. The purpose of this study was to determine the regression model and determine the hazard ratio of each factor that is thought to affect the survival of cervical cancer patients. The results of this study showed that the factors that influence patients with cervical cancer in their survival are stage II and stage III variables (the patient’s stage), complications, and a history of pregnancy (who have children 0-2).
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Moolgavkar, Suresh H., Ellen T. Chang, Heather N. Watson, and Edmund C. Lau. "An Assessment of the Cox Proportional Hazards Regression Model for Epidemiologic Studies." Risk Analysis 38, no. 4 (November 23, 2017): 777–94. http://dx.doi.org/10.1111/risa.12865.

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Heinzl, Harald, Janez Stare, and Martina Mittlböck. "A Measure of Dependence for the Stratified Cox Proportional Hazards Regression Model." Biometrical Journal 44, no. 6 (September 2002): 671–83. http://dx.doi.org/10.1002/1521-4036(200209)44:6<671::aid-bimj671>3.0.co;2-e.

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Xie, Xiaodong, and Shaozhi Zheng. "Group MCP for Cox Models with Time-Varying Coefficients." Journal of Systems Science and Information 4, no. 5 (October 25, 2016): 476–88. http://dx.doi.org/10.21078/jssi-2016-476-13.

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AbstractCox’s proportional hazard models with time-varying coefficients have much flexibility for modeling the dynamic of covariate effects. Although many variable selection procedures have been developed for Coxs proportional hazard model, the study of such models with time-varying coefficients appears to be limited. The variable selection methods involving nonconvex penalty function, such as the minimax concave penalty (MCP), introduces numerical challenge, but they still have attractive theoretical properties and were indicated that they are worth to be alternatives of other competitive methods. We propose a group MCP method that uses B-spline basis to expand coefficients and maximizes the log partial likelihood with nonconvex penalties on regression coefficients in groups. A fast, iterative group shooting algorithm is carried out for model selection and estimation. Under some appropriate conditions, the simulated example shows that our method performs competitively with the group lasso method. By comparison, the group MCP method and group lasso select the same amount of important covariates, but group MCP method tends to outperform the group lasso method in selection of unimportant covariates.
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