On estimating a survival function under the generalized proportional hazards model

2007 ◽  
Vol 137 (4) ◽  
pp. 1161-1172
Author(s):  
Jian-Lun Xu
Keyword(s):  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Survival Function ◽  
Hazards Model

scholarly journals Competing risks’ analysis in cardiovascular disease epidemiology; – Τhe case of ATTICA study (2002-2012)

2020 ◽  
pp. 19-25
Author(s):  
Thomas Tsiampalis ◽  
Demosthenes Panagiotakos
Keyword(s):  
Cardiovascular Disease ◽  
Competing Risks ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Cox Model ◽  
Survival Function ◽  
Gray Model ◽  
Disease Epidemiology ◽  
Hazards Model ◽  
One To One

Background: In studies of all-cause mortality, a one-to-one relation connects the hazard with the survival and as a consequence the regression models which focus on the hazard, such as the proportional hazards model, immediately dictate how the covariates relate to the survival function, as well. However, these two concepts and their one-to-one relation are totally different in the context of competing risks, where the terms of cause-specific hazard and cumulative incidence function appear. Objective: The aim of the present work was to present two of the most popular methods (cause-specific hazard model and Fine & Gray model) through an application on cardiovascular disease epidemiology (CVD), as well as, to narratively review more recent publications, based on either the frequentist, or the Bayesian approach to inference. Methods: A narrative review of the most widely used methods in the competing risks setting was conducted, extended to more recent publications. For the application, our interest lied in modeling the risk of Coronary Heart Disease in the presence of vascular stroke, by using the cause-specific hazard and the Fine & Gray models, two of most commonly encountered approaches. Results-Conclusions: After the implementation of these two approaches in the context of competing risks in CVD epidemiology, it is noted that while the use of the Fine & Gray model includes information about the existence of a competing risk, the interpretation of the results is not as easy as in the case of the cause-specific risk Cox model.

scholarly journals Geometric Method of Determining Hazard for the Continuous Survival Function

2015 ◽  
Vol 15 (1) ◽  
pp. 22-33 ◽  
Author(s):  
Beata Bieszk-Stolorz
Keyword(s):  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Survival Function ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Unemployment Benefit ◽  
Hazards Model ◽  
Multiplicative Effect ◽  
And Gender ◽  
The Relationship

Abstract A basic assumption in proportional intensity models is the proportionality, that each covariate has a multiplicative effect on the intensity. The proportionality assumption is a strong assumption which is not always necessarily reasonable and thus needs to be checked. The survival analysis often employs graphic methods to study hazard proportionality. In this paper a geometrical method for determining the value of the hazard function on the basis of the continuous survival function was proposed. This method can be used to compare the intensity of the event for objects belonging to two subgroups of the analysed population. If we have graphs of survival function, then an analysis of the tangents at a specific time and their roots enables us to find the intensity and to study the relationship between them for different subgroups. This method can also be useful when studying the proportionality of hazard. It is a condition for the use of the Cox proportional hazards model. The above method was used to evaluate the effect of unemployment benefit and gender on unemployment and on the intensity of finding a job.

Survival analysis

2015 ◽  
Author(s):  
Peter Watson
Keyword(s):  
Survival Analysis ◽  
Hazard Function ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Survival Function ◽  
Time Varying ◽  
Odds Ratios ◽  
Duration Time ◽  
Hazards Model ◽  
Data Censoring

This chapter explores survival analysis. It includes data censoring, functions of duration time (the survival function, and hazard function), Cox’s proportional hazards model, log-linearity, time varying predictors, and odds ratios.

Impact of digital multi-sided platforms on firm survival in retail

2021 ◽  
pp. 93-122
Author(s):  
E. S. Andronova ◽  
A. I. Rey ◽  
G. R. Akzhigitova
Keyword(s):  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Positive Impact ◽  
Survival Function ◽  
Firm Survival ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Data Set ◽  
Hazards Model ◽  
Kaplan Meier

This paper explores firm survival in Russian retail industry in cases of digital multi-sided platforms penetration such as aggregator Yandex.Market, marketplace Wildberries, electronic store Ozon and classified-ad service Avito. The panel data set of 130 thousand firms was analyzed using two methods: non-parametric Kaplan—Meier estimator and semi-parametric Cox proportional hazards model with time dependent covariates. Kaplan—Meier estimator calculates the survival function for censored data. Cox proportional hazards model examines the effect of platform penetration on hazard rates of differently sized firms in various industry spheres. Platforms-aggregators Yandex.Market and Wildberries have a strong positive impact on firm survival while platformsdisruptors Ozon and Avito increase likelihood of firm failure. The main results of platform influence in various industry spheres are as follows: the aggregator of price offers has a more positive impact on segments with high information asymmetry; and firms specialized on Wildberries key product categories enjoy lower hazard ratios of bankruptcy or liquidation. These hypotheses are not supported for Ozon and Avito platforms.

HRA Model for Hypercholesterolemia Based on a Longitudinal Health Database

1998 ◽  
Vol 37 (02) ◽  
pp. 130-133
Author(s):  
T. Kishimoto ◽  
Y. Iida ◽  
K. Yoshida ◽  
M. Miyakawa ◽  
H. Sugimori ◽  
...  
Keyword(s):  
Total Cholesterol ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Model Analysis ◽  
Serum Total Cholesterol ◽  
Hazards Model ◽  
The Mean ◽  
Health Database ◽  
Observation Period

AbstractTo evaluate the risk factors for hypercholesterolemia, we examined 4,371 subjects (3,207 males and 1,164 females) who received medical checkups more than twice at an AMHTS in Tokyo during the period from 1976 through 1991; and whose serum total cholesterol was under 250 mg/dl. The mean follow-up duration was 6.6 years. A self-registering questionnaire was administered at the time of the health checkup. The endpoint of this study was the onset of hypercholesterolemia when the level of serum total cholesterol was 250 mg/dl and over. We compared two prognosis groups (normal and hypercholesterol) in terms of age, examination findings and lifestyle. After assessing each variable, we employed Cox's proportional hazards model analysis to determine the factors related to the occurrence of hypercholesterolemia. According to proportional hazards model analysis, total cholesterol, triglyceride and smoking at the beginning, and hypertension during the observation period were selected in males; and total cholesterol at the beginning and age were selected in females to determine the factors related to the occurrence of hypercholesterolemia.

Correlates of Child Mortality in Pakistan: A Hazards Model Analysis

1999 ◽  
Vol 38 (1) ◽  
pp. 85-118 ◽  
Author(s):  
Jennifer Benneti
Keyword(s):  
Child Mortality ◽  
Proportional Hazards ◽  
Contraceptive Use ◽  
Proportional Hazards Model ◽  
Economic Status ◽  
Model Analysis ◽  
Health Seeking ◽  
Large Cities ◽  
Number Of Children ◽  
Hazards Model

This study investigated factors associated with child mortality in an area in Rawalpindi, one of the large cities of Pakistan. Using both demographic and anthropological methods, the research was conducted to specifically examine the processes and mechanisms whereby a link is established between child mortality and its covariates. Controlling for the socio-economic status as a determinant of child mortality, the study population was limited to a lower income stratum living in a homogeneous environment where all households had equal access to health-related and other facilities. Results of the proportional hazards model analysis on 130I index children suggest that non-economic factors like maternal health-seeking behaviour were related to high child mortality. The cultural norm of bearing a large number of children was the most significant correlate. In order of significance, this was followed by contraceptive use, current age of the mother, age at marriage and the hygienic conditions of the household. The study provides strong evidence of familial clustering of mortality by order of the household.

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