An Added Variable Goodness-of-fit Test for the Cox Proportional Hazards Model

2004 ◽  
Vol 46 (3) ◽  
pp. 343-350 ◽  
Author(s):  
Susanne May ◽  
David W. Hosmer
Keyword(s):  
Goodness Of Fit ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Goodness Of Fit Test ◽  
Hazards Model

scholarly journals Cox Proportional Hazards Model for Identification of the Prognostic Factors in the Survival of Acute Liver Failure Patients in India

2018 ◽  
Vol 2 ◽  
pp. 53-74
Author(s):  
Shankar Prasad Khanal ◽  
V. Sreenivas ◽  
S.K. Acharya
Keyword(s):  
Survival Analysis ◽  
Prognostic Factors ◽  
Liver Failure ◽  
Cerebral Edema ◽  
Goodness Of Fit ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Hazards Model

Background: Acute Liver Failure (ALF) is a kind of dangerous rare liver injury among all liver diseases. Different statistical methods such as Logistic regression, Kaplan-Meier estimate of survival function followed by Log-rank test and semi-parametric approaches of survival analysis has been applied in order to identify the significant risk factors of ALF patients. In most of the studies, regression models used in this setup has not been evaluated by model assumptions and their goodness of fit tests.Objective: To apply appropriate survival analysis technique to identify the prognostic factors in the survival of ALF patients, to develop prognostic index, and to predict survival probability for different scenario.Materials and Methods: The study is based on the retrospective cohort study design with altogether 1099 ALF patients taken from the liver clinic, All India Institute of Medical Sciences, New Delhi India. Cox regression has been considered as the suitable model for handling this time to event data, and the assumptions of the model, goodness of fit of the model was assessed and survival probabilities were predicted.Results: This study has identified six prognostic factors namely age, prothrombin time, cerebral edema, total serum bilirubin, serum creatinine and etiology for ALF patients. The hazards of mortality [HR: 2.38; 95% C.I.: (1.99, 2.85), p < 0.001] is the highest for cerebral edema among all these prognostic factors. Nearly 9%, 26%, 39%, 50%, 59% and 63% of ALF patients with a PI of 1, 3, 5, 7, 9 and 10 respectively die by 3 days of hospital stay.Conclusion: The developed Cox Proportional Hazards model with six prognostic factors has satisfied the model assumptions and goodness of fit tests. The risk score and the predicted survival probabilities will be immensely helpful to the hepatologists to make a quick decision regarding the likely prognosis of a patient at admission and helpful in triaging the ALF patients for liver transplant.Nepalese Journal of Statistics, Vol. 2, 53-74

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.

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