Step-function covariate effects in the proportional-hazards model

1995 ◽  
Vol 23 (2) ◽  
pp. 109-129 ◽  
Author(s):  
Michael Leblanc ◽  
John Crowley
Keyword(s):  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Step Function ◽  
Covariate Effects ◽  
Hazards Model

Flexible covariate effects in the proportional hazards model

1992 ◽  
Vol 22 (3) ◽  
pp. 241-250 ◽  
Author(s):  
Treyor Hastie ◽  
Lynn Sleeper ◽  
Robert Tibshirani
Keyword(s):  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Covariate Effects ◽  
Hazards Model

scholarly journals Competing Risks Copula Models for Unemployment Duration: An Application to a German Hartz Reform

2017 ◽  
Vol 6 (1) ◽  
Author(s):  
Simon M.S. Lo ◽  
Gesine Stephan ◽  
Ralf A. Wilke
Keyword(s):  
Competing Risks ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Unemployment Duration ◽  
Copula Models ◽  
Covariate Effects ◽  
Hazards Model ◽  
Special Cases

AbstractThe copula graphic estimator (CGE) for competing risks models has received little attention in empirical research, despite having been developed into a comprehensive research method. In this paper, we bridge the gap between theoretical developments and applied research by considering a general class of competing risks copula models, which nests popular models such as the Cox proportional hazards model, the semiparametric multivariate mixed proportional hazards model (MMPHM), and the CGE as special cases. Analyzing the effects of a German Hartz reform on unemployment duration, we illustrate that the CGE imposes fewer restrictions on partial covariate effects than standard methods do. Differences are less evident when a more flexible difference-in-differences estimator is applied. It is also found that the MMPHM estimates react more strongly to the choice of the copula than the CGE in terms of the shape of the treatment effect function over time. Thus, the MMPHM produces less robust results in our application.

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.

scholarly journals Risk factors associated with major adverse cardiac and cerebrovascular events following percutaneous coronary intervention: a 10-year follow-up comparing random survival forest and Cox proportional-hazards model

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Maryam Farhadian ◽  
Sahar Dehdar Karsidani ◽  
Azadeh Mozayanimonfared ◽  
Hossein Mahjub
Keyword(s):  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Coronary Intervention ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Hazards Model ◽  
Cerebrovascular Events ◽  
Long Term Follow Up ◽  
Stent Length

Abstract Background Due to the limited number of studies with long term follow-up of patients undergoing Percutaneous Coronary Intervention (PCI), we investigated the occurrence of Major Adverse Cardiac and Cerebrovascular Events (MACCE) during 10 years of follow-up after coronary angioplasty using Random Survival Forest (RSF) and Cox proportional hazards models. Methods The current retrospective cohort study was performed on 220 patients (69 women and 151 men) undergoing coronary angioplasty from March 2009 to March 2012 in Farchshian Medical Center in Hamadan city, Iran. Survival time (month) as the response variable was considered from the date of angioplasty to the main endpoint or the end of the follow-up period (September 2019). To identify the factors influencing the occurrence of MACCE, the performance of Cox and RSF models were investigated in terms of C index, Integrated Brier Score (IBS) and prediction error criteria. Results Ninety-six patients (43.7%) experienced MACCE by the end of the follow-up period, and the median survival time was estimated to be 98 months. Survival decreased from 99% during the first year to 39% at 10 years' follow-up. By applying the Cox model, the predictors were identified as follows: age (HR = 1.03, 95% CI 1.01–1.05), diabetes (HR = 2.17, 95% CI 1.29–3.66), smoking (HR = 2.41, 95% CI 1.46–3.98), and stent length (HR = 1.74, 95% CI 1.11–2.75). The predictive performance was slightly better by the RSF model (IBS of 0.124 vs. 0.135, C index of 0.648 vs. 0.626 and out-of-bag error rate of 0.352 vs. 0.374 for RSF). In addition to age, diabetes, smoking, and stent length, RSF also included coronary artery disease (acute or chronic) and hyperlipidemia as the most important variables. Conclusion Machine-learning prediction models such as RSF showed better performance than the Cox proportional hazards model for the prediction of MACCE during long-term follow-up after PCI.

scholarly journals Influence of Tourism Seasonality and Financial Ratios on Hotels’ Exit Risk

2021 ◽  
pp. 109634802110160
Author(s):  
Dengjun Zhang ◽  
Jinghua Xie
Keyword(s):  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Hotel Industry ◽  
Financial Ratios ◽  
Market Segment ◽  
Tourism Demand ◽  
Hazards Model ◽  
Two Measures ◽  
The Impact

Tourism seasonality negatively affects hotels’ operational and financial performance and then survival probabilities. Several studies have evaluated the impact of tourism seasonality on hotels’ exit risk. However, the empirical findings are ambiguous, probably due to the overall seasonality and different measures used in these studies. Against this background, this study explores the impact of tourism seasonality on hotel firms’ exit risk, using a proportional hazards model. We controlled for financial ratios, the main factors influencing the exit risk, and used two measures of tourism seasonality by market segment, namely, leisure, business, and conference tourism. The case study is the Norwegian hotel industry. The empirical results suggest that the different seasonal patterns of tourism demand in the market segments mitigate the impact of the overall seasonality on hotels’ exit risk, and that seasonality measures of various tourism segments affect the exit risk in different ways.

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