Survival analysis under the Cox proportional hazards model with pooled covariates

2020 ◽  
Author(s):  
Paramita Saha‐Chaudhuri ◽  
Lamin Juwara
Keyword(s):  
Survival Analysis ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Hazards Model

scholarly journals Evaluation of Changes on World Stock Exchanges in Connection with the SARS-CoV-2 Pandemic. Survival Analysis Methods

Risks ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 121
Author(s):  
Beata Bieszk-Stolorz ◽  
Krzysztof Dmytrów
Keyword(s):  
Survival Analysis ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Stock Exchange ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Stock Exchanges ◽  
Analysis Methods ◽  
Hazards Model ◽  
Kaplan Meier

The aim of our research was to compare the intensity of decline and then increase in the value of basic stock indices during the SARS-CoV-2 coronavirus pandemic in 2020. The survival analysis methods used to assess the risk of decline and chance of rise of the indices were: Kaplan–Meier estimator, logit model, and the Cox proportional hazards model. We observed the highest intensity of decline in the European stock exchanges, followed by the American and Asian plus Australian ones (after the fourth and eighth week since the peak). The highest risk of decline was in America, then in Europe, followed by Asia and Australia. The lowest risk was in Africa. The intensity of increase was the highest in the fourth and eleventh week since the minimal value had been reached. The highest odds of increase were in the American stock exchanges, followed by the European and Asian (including Australia and Oceania), and the lowest in the African ones. The odds and intensity of increase in the stock exchange indices varied from continent to continent. The increase was faster than the initial decline.

scholarly journals Survival Analysis of COVID-19 on Democracy with Cox Proportional Hazards Model

2020 ◽  
Author(s):  
Yue Zhao ◽  
Deepika Dilip
Keyword(s):  
United States ◽  
Survival Analysis ◽  
South Korea ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
The United States ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Effective Measure ◽  
Hazards Model

Abstract Background: The outbreak of Coronavirus disease 2019 (COVID-19) has struck us in many ways and we observed that China and South Korea found an effective measure to contain the virus. Conversely, the United States and the European countries are struggling to fight the virus. China is not considered a democracy and South Korea is less democratic than the United States. Therefore, we want to explore the association between the deaths of COVID-19 and democracy. Methods: We collected COVID-19 deaths data for each country from the Johns Hopkins University website and democracy indices of 2018 from the Economist Intelligence Unit website in May 2020. Then we conducted a survival analysis, regarding each country as a subject, with the Cox Proportional Hazards Model, adjusting for other selected variables. Result: The result showed that the association between democracy and deaths of COVID-19 was significant (P=0.04), adjusting for other covariates. Conclusion: In conclusion, less democratic governments performed better in containing the virus and controlling the number of deaths.

scholarly journals Survival Analysis for the Adjustment Phase Following Investment in Swiss Dairy Sheds

Agriculture ◽  
2019 ◽  
Vol 9 (11) ◽  
pp. 238
Author(s):  
Kramer ◽  
Schorr ◽  
Doluschitz ◽  
Lips
Keyword(s):  
Survival Analysis ◽  
Proportional Hazards ◽  
Planning Process ◽  
Proportional Hazards Model ◽  
Cox Proportional Hazards ◽  
Equity Capital ◽  
Cox Proportional Hazards Model ◽  
Farm Income ◽  
Hazards Model ◽  
Farm Accountancy Data Network

We analysed the adjustment phase following a dairy shed investment. On the basis of farm observations from both the Swiss Farm Accountancy Data Network (FADN) and a database of government-supported investments from 2003 through 2014, we focused on the imputed profit, the farm income minus opportunity costs for family labour and family capital. After investment, the analysed farms needed three years to return to the same profit level as that before the investment (median value). A Cox proportional-hazards model (survival analysis) showed that the probability of reattaining the imputed profit increased with equity capital. A reduction of the probability was related to a high imputed profit, a high off-farm income, high expenses for purchased animals and, in particular, a greater use of family labour before the investment. We conclude that the use of family labour after investment should be addressed more thoroughly during the planning process prior to an investment.

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 442. Sex-Related Differences in Mortality from COVID-19: Survival Analysis of Patients from an Urban Hospital

2021 ◽  
Vol 8 (Supplement_1) ◽  
pp. S323-S323
Author(s):  
Mamta Sharma ◽  
Susan M Szpunar ◽  
Ashish Bhargava ◽  
Leonard B Johnson ◽  
Louis Saravolatz
Keyword(s):  
Risk Factors ◽  
Survival Analysis ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Factors Associated ◽  
Hazards Model ◽  
Cox Proportional Hazards Models ◽  
Failure Assessment

Abstract Background Mortality from COVID-19 is associated with male sex, older age, black race, and comorbidities including obesity. Our study identified risk factors for in-hospital mortality from COVID-19 using survival analysis at an urban center in Detroit, MI. Methods This was a single-center historical cohort study. We reviewed the electronic medical records of patients positive for severe acute respiratory syndrome coronavirus 2 (the COVID-19 virus) on qualitative polymerase-chain-reaction assay, who were admitted between 3/8-6/14/20. We assessed risk factors for mortality using Kaplan-Meier analysis and Cox proportional hazards models. Results We included 565 patients with mean age (standard deviation) 64.4 (16.2) years, 52.0% male (294) and 77.2% (436) black/African American. The overall mean body mass index (BMI) was 32.0 (9.02) kg/m2. At least one comorbidity was present in 95.2% (538) of patients. The overall case-fatality rate was 30.4% (172/565). The unadjusted mortality rate among males was 33.7% compared to 26.9% in females (p=0.08); the median time to death (range) for males was 16.8 (0.3, 33.9) compared to 14.2 (0.32, 47.7) days for females (p=0.04). Univariable survival analysis with Cox proportional hazards models revealed that age (p=&lt; 0.0001), admission from a facility (p=0.002), public insurance (p&lt; 0.0001), respiratory rate ≥ 22 bpm (p=0.02), lymphocytopenia (p=0.07) and serum albumin (p=0.007) were additional risk factors for mortality (Table 1). From multivariable Cox proportional hazards modeling (Table 2), after controlling for age, Charlson score and qSofa, males were 40% more likely to die than females (p=0.03). Table 1. Univariate analysis with Cox proportional hazards model on factors associated with mortality in patients with COVID-19 Abbreviations: HR: Hazard ratio, CI: Confidence interval Table 2. Multivariable analysis with Cox proportional hazards model on factors associated with mortality in patients with COVID-19 Abbreviations: HR: Hazard ratio, CI: Confidence interval, CWIC: Charlson weighted index of comorbidity, qSOFA: Quick sepsis related organ failure assessment Conclusion After controlling for risk factors for mortality including age, comorbidity and sepsis-related organ failure assessment, males continued to have a higher hazard of death. These demographic and clinical factors may help healthcare providers identify risk factors from COVID-19. Disclosures All Authors: No reported disclosures

scholarly journals Adapting the Default Weighted Survival Analysis Modelling Approach to Model IFRS 9 LGD

Risks ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 103
Author(s):  
Morne Joubert ◽  
Tanja Verster ◽  
Helgard Raubenheimer ◽  
Willem D. Schutte
Keyword(s):  
Survival Analysis ◽  
Financial Reporting ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Survival Curve ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Reporting Standard ◽  
International Financial Reporting Standard ◽  
Ifrs 9

Survival analysis is one of the techniques that could be used to predict loss given default (LGD) for regulatory capital (Basel) purposes. When using survival analysis to model LGD, a proposed methodology is the default weighted survival analysis (DWSA) method. This paper is aimed at adapting the DWSA method (used to model Basel LGD) to estimate the LGD for International Financial Reporting Standard (IFRS) 9 impairment requirements. The DWSA methodology allows for over recoveries, default weighting and negative cashflows. For IFRS 9, this methodology should be adapted, as the estimated LGD is a function of in the expected credit losses (ECL). Our proposed IFRS 9 LGD methodology makes use of survival analysis to estimate the LGD. The Cox proportional hazards model allows for a baseline survival curve to be adjusted to produce survival curves for different segments of the portfolio. The forward-looking LGD values are adjusted for different macro-economic scenarios and the ECL is calculated for each scenario. These ECL values are probability weighted to produce a final ECL estimate. We illustrate our proposed IFRS 9 LGD methodology and ECL estimation on a dataset from a retail portfolio of a South African bank.

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