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

Effects of Collinearity on Cox Proportional Hazard Model with Time Dependent Coefficients: A Simulation Study

2020 ◽  
Author(s):  
B. T. Babalola ◽  
W. B. Yahya
Keyword(s):  
Simulation Study ◽  
Cox Model ◽  
Hazard Model ◽  
Time Dependent ◽  
Cox Proportional Hazard Model ◽  
Proportional Hazard ◽  
Proportional Hazard Model ◽  
Cox Proportional Hazard ◽  
Correlation Structures ◽  
Time Dependent Covariates

Background: The Cox proportional hazard model has gained ground in Biostatistics and other related fields. It has been extended to capture different scenarios, part of which are violation of the proportionality of the hazards, presence of time dependent covariates and also time dependent co-efficients. This paper focuses on the behaviour of the Cox Model in relation to time coefficients in the presence of different levels of collinearity. Objectives: The objectives of this study are to examine the effects of collinearity on the estimates of time dependent co-effiecients in Cox proportional hazard model and to compare the estimates of the model for the logarithm and the square functions of time. Materials and methods: The Algorithm based on a binomial model was extended in order to incorporate the different correlation structures required for the study. The scaled Schoenfeld residuals plots revealed the behaviour of the estimated betas at different degrees of collinearity. Results and conclusions are based of outcome of simulation study performed only. Results: The estimated betas were compared to the true betas at the different level of collinearity in graphical pattern. Conclusion: The study shows that collinearity is a huge factor that influences the correctness of the estimates of the regressors within the framework of Cox model.

scholarly journals A Study of Factors Affecting the Length of Hospital Stay of COVID-19 Patients by Cox-Proportional Hazard Model in a South Indian Tertiary Care Hospital

2021 ◽  
Vol 12 ◽  
pp. 215013272110002
Author(s):  
Gayathri Thiruvengadam ◽  
Marappa Lakshmi ◽  
Ravanan Ramanujam
Keyword(s):  
Hospital Stay ◽  
Tertiary Care ◽  
Length Of Hospital Stay ◽  
Hazard Model ◽  
Cox Proportional Hazard Model ◽  
Care Hospital ◽  
Proportional Hazard ◽  
Proportional Hazard Model ◽  
Cox Proportional Hazard ◽  
South Indian

Background: The objective of the study was to identify the factors that alter the length of hospital stay of COVID-19 patients so we have an estimate of the duration of hospitalization of patients. To achieve this, we used a time to event analysis to arrive at factors that could alter the length of hospital stay, aiding in planning additional beds for any future rise in cases. Methods: Information about COVID-19 patients was collected between June and August 2020. The response variable was the time from admission to discharge of patients. Cox proportional hazard model was used to identify the factors that were associated with the length of hospital stay. Results: A total of 730 COVID-19 patients were included, of which 675 (92.5%) recovered and 55 (7.5%) were considered to be right-censored, that is, the patient died or was discharged against medical advice. The median length of hospital stay of COVID-19 patients who were hospitalized was found to be 7 days by the Kaplan Meier curve. The covariates that prolonged the length of hospital stay were found to be abnormalities in oxygen saturation (HR = 0.446, P < .001), neutrophil-lymphocyte ratio (HR = 0.742, P = .003), levels of D-dimer (HR = 0.60, P = .002), lactate dehydrogenase (HR = 0.717, P = .002), and ferritin (HR = 0.763, P = .037). Also, patients who had more than 2 chronic diseases had a significantly longer length of stay (HR = 0.586, P = .008) compared to those with no comorbidities. Conclusion: Factors that are associated with prolonged length of hospital stay of patients need to be considered in planning bed strength on a contingency basis.

scholarly journals SMOKING STATUS AFFECTING SURVIVAL OF ADENOCARCINOMA LUNG CANCER PATIENTS IN KUALA LUMPUR, MALAYSIA

2017 ◽  
Vol 10 (9) ◽  
pp. 312
Author(s):  
Nida Sajid Ali Bangash ◽  
Natasha Hashim ◽  
Nahlah Elkudssiah Ismail
Keyword(s):  
Lung Cancer ◽  
Prognostic Significance ◽  
Hazard Model ◽  
Rank Test ◽  
Cox Proportional Hazard Model ◽  
Proportional Hazard ◽  
Kuala Lumpur ◽  
Proportional Hazard Model ◽  
Cox Proportional Hazard ◽  
Survival Difference

  Objective: Adenocarcinoma (AC) of the lung is now the most common histologic type of non-small cell lung cancer (NSCLC) worldwide since the past 20 years. This study was conducted to investigate survival difference among smoker and non-smoker lung AC patients.Methods: A retrospective observational study was conducted for 81 advanced NSCLC adult Malaysian patients in Radiotherapy and Oncology Clinic at Hospital Kuala Lumpur, Malaysia. A total of adult 30 Malaysian smokers and 51 non-smokers with lung AC were included. Ex-smokers were not included in the study. Demographic and clinical data were collected and described. For survival analysis, Kaplan–Meier test and log-rank test were used to calculate overall survival (OS) and analyse the difference in the survival curve. Cox proportional hazard model was used to identify prognostic significance of smoking status.Results: Non-smokers showed a significant association with female gender and Stage IV NSCLC. The median OS was higher for non-smokers (493 days) as compared to smokers (230 days). The Cox proportional hazard model showed higher hazard ratio for smokers.Conclusion: Non-smoking is an independent positive prognostic factor in lung AC.

scholarly journals Introduction of Two-Dose Mumps-Containing Vaccine into Routine Immunization Schedule in Quzhou, China, Using Cox-Proportional Hazard Model

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Zhiying Yin ◽  
Canjie Zheng ◽  
Quanjun Fang ◽  
Xiaoying Gong ◽  
Guoping Cao ◽  
...  
Keyword(s):  
Hazard Model ◽  
Routine Immunization ◽  
Cox Proportional Hazard Model ◽  
Proportional Hazard ◽  
Immunization Schedule ◽  
Proportional Hazard Model ◽  
Cox Proportional Hazard ◽  
Kaplan Meier ◽  
Routine Immunization Schedule ◽  
Different Doses

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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