A computational method for estimating a change point in the Cox hazard model

2021 ◽  
Author(s):  
G. Y. Arenas ◽  
J. A. Villaseñor ◽  
O. Palmeros ◽  
F. Tajonar
Keyword(s):  
Change Point ◽  
Hazard Model ◽  
Computational Method ◽  
Cox Hazard Model

scholarly journals Factors influencing the going private decision - a Hazard model approach

2016 ◽  
Vol 13 (2) ◽  
pp. 532-545
Author(s):  
Lucia Ehn
Keyword(s):  
Corporate Governance ◽  
Research Question ◽  
Hazard Model ◽  
Private Companies ◽  
Going Private ◽  
Factors Influencing ◽  
Logistic Regressions ◽  
Cox Hazard Model ◽  
Model Approach

The aim of this paper is to characterize companies which voluntarily changed their ownership from public to private. The research question addressed in this paper is, if it is possible to characterize going private companies in earlier stages than just shortly before the announcement of their step into privacy. I therefore examine going private companies in a lifecycle context with Cox hazard model and conduct additional logistic regressions at the time of the IPO and shortly before delisting. Further, I not only focus on companies’ fundamentals, but also on perceptibility and corporate governance variables. With data of 1’184 US IPOs from 1990 to 2013, my results show that both, perceptibility and corporate governance variables accelerate the going private decision.

The role of S-1 in gemcitabine-refractory pancreatic cancer: A retrospective single-institution study

2009 ◽  
Vol 27 (15_suppl) ◽  
pp. e15648-e15648
Author(s):  
Y. Nakai ◽  
H. Isayama ◽  
T. Sasaki ◽  
N. Sasahira ◽  
K. Hirano ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Prognostic Factors ◽  
Hazard Model ◽  
Second Line ◽  
Second Line Chemotherapy ◽  
Gemcitabine Refractory ◽  
The University ◽  
Cox Hazard Model ◽  
The Impact ◽  
Line Chemotherapy

e15648 Background: S-1 was reported to be active against gemcitabine (Gem)-refractory pancreatic cancer (PaC) in Japan and was introduced in February 2005 in our institution. The aim of this study was to elucidate the impact of S-1 on prognosis of patients with Gem- refractory PaC. Methods: A total of 108 patients (pts) with advanced PaC who were treated with Gem and had disease progression (PD) at the University of Tokyo Hospital were analyzed. The introduction rates of second-line chemotherapy and the causes of introduction failure were assessed. Prognostic factors for residual survival (RS) for Gem-refractory PaC were analyzed by the Cox proportional hazard model. Results: Of 108 pts with Gem-refractory PaC, 47 pts (PreS-1 Group) had PD before February 2005, the time of S-1 introduction in our institution, and 61 pts (PostS-1 Group) after February 2005. There were no differences in baseline characteristics at PD for Gem between PreS-1 and PostS-1 Groups, except for metastasis to peritoneum more prevalent in PreS-1 Group (44.7% in PreS-1 Group and 23.0% in PostS-1 Group, p=0.023). The introduction rate of second-line chemotherapy increased from 12.8% in PreS-1 Group to 45.9% in PostS-1 Group. Second-line chemotherapy was administered in 34 pts, 29 by S-1, 4 by 5-FU-based chemoradiation, and 1 by 5-FU. The causes of introduction failure of second line chemotherapy were poor PS in 64.9%, patients’ refusal in 16.2%, infection in 2.7%, adverse effects of Gem in 1.4% and jaundice in 1.4%. RR, PFS, and OS for second-line S-1 were 17.2%, 2.5 Mo, and 7.8 Mo, respectively. PFS for Gem was not prognostic of PFS for S-1 (2.5 Mo both in pts with PFS >6Mo and in pts with PFS <6Mo for Gem). RS after PD for Gem was prolonged from 3.1 Mo in PreS-1 Group to 6.5 Mo in PostS-1 Group (p<0.001). The Cox hazard model revealed PreS-1 Group (HR2.42, p=0.001) in addition to male gender (HR1.83, p=0.019), poor PS (HR3.52, p<0.001), liver metastasis (HR2.36, p=0.037), elevated LDH (per 100U/L increase) (HR 1.30, p=0.046), elevated CRP (HR 1.14, p=0.023) at PD for Gem as poor prognostic factors of RS for Gem-refractory PaC. Conclusions: Introduction of S-1 might lead to improvement of prognosis in patients with Gem-refractory PaC. No significant financial relationships to disclose.

Risks of P2P Lending Platforms in China: Modeling Failure Using a Cox Hazard Model

2016 ◽  
Vol 49 (3) ◽  
pp. 161-172 ◽  
Author(s):  
Jianjun Li ◽  
Sara Hsu ◽  
Zhang Chen ◽  
Yang Chen
Keyword(s):  
Hazard Model ◽  
P2p Lending ◽  
Cox Hazard Model

On computing estimates of a change-point in the Weibull regression hazard model

2017 ◽  
Vol 45 (4) ◽  
pp. 642-648 ◽  
Author(s):  
Oscar Palmeros ◽  
Jose A. Villaseñor ◽  
Elizabeth González
Keyword(s):  
Change Point ◽  
Hazard Model ◽  
Weibull Regression

scholarly journals Modelling non-life insurance in Sri Lanka using Cox Hazard Model and classification of risky customers

2020 ◽  
Vol 11 (2) ◽  
pp. 118
Author(s):  
W. Ajith R. De Mel ◽  
W. A. P. A. Chathurangani
Keyword(s):  
Sri Lanka ◽  
Life Insurance ◽  
Hazard Model ◽  
Cox Hazard Model

scholarly journals Application of Survival Analysis of TB Patients Using Parametric Model: A Case Study of General Hospital Bayara

2020 ◽  
pp. 54-65
Author(s):  
Samson Daniel ◽  
K. E. Lasisi ◽  
Jerry Banister
Keyword(s):  
Goodness Of Fit ◽  
Smoking Habit ◽  
Secondary Data ◽  
Frailty Model ◽  
Hazard Model ◽  
Parametric Models ◽  
Distribution Model ◽  
Weight Category ◽  
Gamma Frailty ◽  
Cox Hazard Model

Aim: We evaluate the performance of parametric models, mixture of generalized gamma frailty model with Gompertz distribution and compare it with Cox proportional hazard model that is commonly used in the analysis of TB patients and also by [1]. Place and the Duration of the Study: The study was carried out in Bauchi State, Nigeria from January, 2017 to January, 2020. Methodology: In this study secondary data was used and gotten from the patients’ treatment card and TB registers from January 2015 to December 2017. The covariates used were, drug, age, marital status, smoking habit, educational level, weight, category, and risk factor. We used AIC and BIC selection tool to select the model with the lowest value and then compare it with Cox hazard model. Data analysis was done in Stata version 14. Results: The result of the analysis shows that mixture of frailty model with Gompertz baseline distribution has the lowest AIC and BIC value when compared to Cox Proportional model therefore shows a better goodness of fit for our dataset. Conclusion: We therefore conclude that mixture of frailty model with Gompertz baseline distribution model can serve as an alternative to Cox Proportional Model.

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