scholarly journals Penanganan Ties Event dalam Regresi Cox Proportional Hazard Menggunakan Metode Breslow (Kasus: Pasien Rawat Inap DBD di RSAL Jala Ammari Makassar)

2020 ◽  
Vol 2 (1) ◽  
pp. 13
Author(s):  
Herawati Hafid ◽  
Muhammad Nadjib Bustan ◽  
Muhammad Kasim Aidid
Keyword(s):  
Survival Analysis ◽  
Dengue Hemorrhagic Fever ◽  
Recovery Rate ◽  
Cox Model ◽  
Hemorrhagic Fever ◽  
Hazard Ratio ◽  
Partial Likelihood ◽  
P Value ◽  
Proportional Hazard ◽  
Cox Proportional Hazard

Abstrak Analisis Survival adalah prosedur statistika yang digunakan untuk menganalisis data dimana peubah yang diperhatikan adalah waktu sampai terjadinya suatu event. Waktu dapat dinyatakan dalam hitungan hari, minggu, bulan dan tahun. Salah satu tujuan dari analisis survival adalah untuk mengetahui hubungan antara waktu kejadian  peubah bebas yang terukur pada saat dilakukan penelitian. Metode yang sering digunakan dalam analisis survival khususnya data kesehatan adalah Regresi Cox Proportional Hazard (PH) karena distribusinya tidak tergantung pada asumsi waktu kejadian. Dalam suatu data seperti data pasien penderita penyakit Demam Berdarah Dengue (DBD) ditemukan adanya data kejadian bersama (ties event) yang berpengaruh pada pembentukan himpunan risikonya pada bagian estimasi parameter model cox,pada kasus kejadian bersama (ties event) dilakukan modifikasi pada partial likelihood untuk mengetahui faktor-faktor yang mempengaruhi laju kesembuhan pasien penderita penyakit DBD. Adapun hasil analisisnya, diperoleh faktor yang paling berpengaruh terhadap laju kesembuhan penyakit DBD yakni leukosit dengan p-value =0,097< α 0,05, dan nilai hazard ratio sebesar 1,1024 serta faktor yang kedua yaitu hematokrit dengan p-value =0,0141< α 0,05, dan nilai hazard ratio sebesar 1,595. Kata Kunci: Analisis Survival, Regresi Cox PH, Ties Event, Metode Breslow, Demam Berdarah Dengue (DBD). Abstract Survival analysis is a statistical procedure that is used to analyze data where the variables considered are the time until the occurrence of an event. Time can be expressed in days, weeks, months and years. One of the objectives of survival analysis is to find out the relationship between the time of occurrence of independent variables measured at the time of the study. The method often used in survival analysis, especially health data, is Cox Proportional Hazard (PH) Regression because its distribution does not depend on the assumption of the time of the event. In a data such as data on patients with Dengue Hemorrhagic Fever (DHF) data, there were ties event data that influenced the formation of risk sets in the cox model parameter estimation section, in the case of ties event modifications were made to the partial likelihood for know the factors that influence the recovery rate of patients with DHF. As for the results of the analysis, the factors that most influence the recovery rate of leucocyte dengue fever with p-value = 0,097 < α = 0,05 and the hazard ratio of 1.1024 and the second factor is the hematocrit with p-value = 0,0141 < α = 0,05 and the hazard ratio valueamounting to 1,595. Keywords: Survival Analysis, Cox PH Regression, Ties Event, Breslow Method, Dengue Hemorrhagic Fever (DHF).

scholarly journals ANALISIS KETAHANAN HIDUP PENDERITA DENGUE HEMORRHAGIC FEVER (DEMAM BERDARAH) DENGAN REGRESI COX KEGAGALAN PROPORSIONAL (Studi Kasus : Rumah Sakit Islam Nahdlatul Ulama Demak)

2021 ◽  
Vol 10 (3) ◽  
pp. 367-376
Author(s):  
Putri Qodar Ummayah ◽  
Sudarno Sudarno ◽  
Budi Warsito
Keyword(s):  
Survival Analysis ◽  
Dengue Fever ◽  
Dengue Hemorrhagic Fever ◽  
Recovery Time ◽  
Hemorrhagic Fever ◽  
Proportional Hazard ◽  
Hazard Regression ◽  
Cox Proportional Hazard ◽  
Cox Proportional Hazard Regression ◽  
The Relationship

Dengue hemorrhagic fever is an acute febrile disease caused by the dengue virus, which enters the human bloodstream through the bite of a mosquito of the genus Aedes Aegypti or Aedes Albopictus. Based on World Health Organization (WHO) records, it is estimated that 500,000 dengue hemorrhagic fever patients require hospital treatment every year and most of the sufferers are children. To analyze the relationship between recovery time in dengue fever patients and the factors that influence it using regression analysis, the dependent variable is the failure time and the function of the response variable tends to fail constant so to find out the relationship using Cox proportional hazard regression. Cox proportional hazard regression is a regression model that is often used in survival analysis. Survival analysis is a method used to describe data analysis in terms of time from the time of origin defined until a certain event occurs. In this study, the recovery time of dengue fever patients as a function of failure is proportional. The observations used by the researchers for each patient were not the same. The population of this study were all patients with dengue fever. The data used was obtained from the medical record section for data on the length of hospitalization of patients regarding the recovery of patients with dengue fever. The conclusion of the research shows that the factors that affect the recovery time of dengue fever patients are hematocrit, platelets, immunoglobulin G, and immunoglobulin M. 

scholarly journals Survival analysis of Indonesian banking companies

2020 ◽  
Vol 5 (2) ◽  
pp. 39-47
Author(s):  
Farida Titik Kristanti
Keyword(s):  
Survival Analysis ◽  
Financial Distress ◽  
Cox Model ◽  
Hazard Analysis ◽  
Jel Classification ◽  
Proportional Hazard ◽  
Factors Affecting ◽  
Financial Status ◽  
Cox Proportional Hazard ◽  
The Government

Objective – Financial distress is an undesirable condition for any company. To avoid financial distress, and improve the overall financial status of a company, an understanding of the factors affecting financial distress is necessary. This research aims to identify the determinants of banking financial distress. Methodology/Technique – In this study, 41 banks comprised the sample, selected using purposive sampling. The survival cox proportional hazard analysis method to identify the determinant factors of survival of Indonesian Banks. Findings –The results show that that macro indicators (inflation and economic growth) have a significant effect on the banks’ financial distress. This implies that the government as a regulator must maintain the level of growth and inflation that stabilizes the economy so that banks can avoid financial distress. As for the banks’ management, they have an obligation to support government policies in maintaining growth and inflation. Novelty – The study uses the cox proportional hazard model. Type of Paper: Empirical. JEL Classification: G2O, G33. Keywords: Bank; Cox Model; Financial Distress; Survival Analysis. Reference to this paper should be made as follows: Kristanti, F.T. 2020. Survival analysis of Indonesian banking companies, J. Fin. Bank. Review, 5 (2): 39 – 47 https://doi.org/10.35609/jfbr.2020.5.2(1)

scholarly journals Fast Lasso method for Large-scale and Ultrahigh-dimensional Cox Model with applications to UK Biobank

2020 ◽  
Author(s):  
Ruilin Li ◽  
Christopher Chang ◽  
Johanne Marie Justesen ◽  
Yosuke Tanigawa ◽  
Junyang Qian ◽  
...  
Keyword(s):  
Large Scale ◽  
Likelihood Function ◽  
Cox Model ◽  
Partial Likelihood ◽  
Parameter Estimates ◽  
Proportional Hazard ◽  
Uk Biobank ◽  
Cox Proportional Hazard ◽  
The Uk ◽  
Lasso Method

AbstractWe develop a scalable and highly efficient algorithm to fit a Cox proportional hazard model by maximizing the L1-regularized (Lasso) partial likelihood function, based on the Batch Screening Iterative Lasso (BASIL) method developed in (Qian et al. 2019). The output of our algorithm is the full Lasso path, the parameter estimates at all predefined regularization parameters, as well as their validation accuracy measured using the concordance index (C-index) or the validation deviance. To demonstrate the effectiveness of our algorithm, we analyze a large genotype-survival time dataset across 306 disease outcomes from the UK Biobank (Sudlow et al. 2015). Our approach, which we refer to as snpnet-Cox, is implemented in a publicly available package.

scholarly journals Fast Lasso method for large-scale and ultrahigh-dimensional Cox model with applications to UK Biobank

Biostatistics ◽  
2020 ◽  
Author(s):  
Ruilin Li ◽  
Christopher Chang ◽  
Johanne M Justesen ◽  
Yosuke Tanigawa ◽  
Junyang Qiang ◽  
...  
Keyword(s):  
Large Scale ◽  
Likelihood Function ◽  
Cox Model ◽  
Partial Likelihood ◽  
Parameter Estimates ◽  
Proportional Hazard ◽  
Uk Biobank ◽  
Cox Proportional Hazard ◽  
The Uk ◽  
Lasso Method

Summary We develop a scalable and highly efficient algorithm to fit a Cox proportional hazard model by maximizing the $L^1$-regularized (Lasso) partial likelihood function, based on the Batch Screening Iterative Lasso (BASIL) method developed in Qian and others (2019). Our algorithm is particularly suitable for large-scale and high-dimensional data that do not fit in the memory. The output of our algorithm is the full Lasso path, the parameter estimates at all predefined regularization parameters, as well as their validation accuracy measured using the concordance index (C-index) or the validation deviance. To demonstrate the effectiveness of our algorithm, we analyze a large genotype-survival time dataset across 306 disease outcomes from the UK Biobank (Sudlow and others, 2015). We provide a publicly available implementation of the proposed approach for genetics data on top of the PLINK2 package and name it snpnet-Cox.

scholarly journals ANALISIS SURVIVAL UNTUK DURASI PROSES KELAHIRAN MENGGUNAKAN MODEL REGRESI HAZARD ADDITIF

2020 ◽  
Vol 9 (4) ◽  
pp. 402-410
Author(s):  
Triastuti Wuryandari ◽  
Sri Haryatmi Kartiko ◽  
Danardono Danardono
Keyword(s):  
Survival Data ◽  
Cox Model ◽  
Hazard Model ◽  
Partial Likelihood ◽  
Cox Proportional Hazard Model ◽  
Proportional Hazard ◽  
Proportional Hazard Model ◽  
Birth Process ◽  
Cox Proportional Hazard ◽  
Additive Hazard Model

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

scholarly journals ANALISIS SURVIVAL DENGAN PEMODELAN REGRESI COX PROPORTIONAL HAZARD MENGGUNAKAN PENDEKATAN BAYESIAN (Studi Kasus: Pasien Rawat Inap Penderita Demam Tifoid di RSUD Haji Makassar)

2020 ◽  
Vol 2 (2) ◽  
pp. 62
Author(s):  
Adi Rahmat Faisal ◽  
Muhammad Nadjib Bustan ◽  
Suwardi Annas
Keyword(s):  
Survival Analysis ◽  
Hazard Ratio ◽  
Proportional Hazard ◽  
Cox Proportional Hazard

Abstrak. Analisis survival merupakan metode statistika yang digunakan untuk menganalisis data dimana peubah yang diperhatikan adalah waktu sampai terjadinya suatu event. Waktu dapat dinyatakan dalam tahun, bulan, minggu, atau hari dari awal mula dilakukan pengamatan pada seorang individu sampai suatu peristiwa terjadi pada individu. Salah satu tujuan analisis survival adalah untuk mengetahui hubungan antara waktu kejadian dan peubah bebas yang terukur pada saat dilakukan penelitian. Salah satu pendekatan metode regersi yang bisa digunakan adalah regresi Cox Proportional Hazard. Data yang digunakan dalam penelitian ini adalah data pasien penderita demam tifoid di RSUD Haji Makassar. Data demam tifoid memiliki karakteristik yang memungkinkan untuk dilakukan analisis dengan menggunakan regresi Cox Proportional Hazard. Adapun analisisnya menggunakan pendugaan parameter Bayesian, diperoleh faktor yang signifikan berpengaruh terhadap laju kesembuhan pasien adalah nyeri ulu hati. Nilai hazard ratio peubah nyeri ulu hati sebesar 0,63. Nilai tersebut <1 sehingga dapat dikatakan bahwa pasien yang mengalami nyeri ulu hati memiliki laju kesembuhan 0,63 kali dibandingkan yang tidak mengalami nyeri ulu hati.Kata Kunci: Survival Analysis, Regression Cox Proportional Hazard, Thyfoid Fever

Railway Passengers Travel Time Research Based on Survival Analysis

2014 ◽  
Vol 962-965 ◽  
pp. 2580-2583
Author(s):  
Ya Chen Zhao ◽  
Zhen Yu Zhang ◽  
Qing Jie Zheng
Keyword(s):  
Survival Analysis ◽  
Travel Time ◽  
Significant Influence ◽  
Hazard Rate ◽  
Family Income ◽  
Proportional Hazard ◽  
Rate Model ◽  
Analysis Method ◽  
Cox Proportional Hazard ◽  
Hazard Rate Model

With the development of the economy, people have higher request for the time. Studying the choice of travel about rail passengers becomes more significant. Due to these problem above and using survival analysis method, this paper builds travel time survival model based on questionnaire and have a whole analysis of the travel time of the rail passenger. Then, it concludes that most of the rail passengers’ travel time is below five hours. At last, this paper builds COX proportional hazard rate model of travel time and study the factors about travel time. The result demonstrates that the factor about whether it is students or not, family income, whether it is travelling and the number of packages has a significant influence on the travel time.

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