An overview of survival analysis with an application in the credit risk environment

Smuts, Marius; Allison, James S.

doi:10.5784/36-2-690

Cited by 3 publications

(5 citation statements)

References 27 publications

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“…The duration of the follow-up period from the defined start point to the occurrence of a specific event, from the start of the remission phase to its conclusion, or from the time of disease diagnosis, is measured using overall survival analysis. [9][10][11][12][13][14][15][16][17] Any event that can happen to a person can be of interest, including death, the development of an illness, its recurrence or recurrence after it has been treated, convalescence, or anything else. [18][19][20][21][22][23] In the analysis of resilience, censored data always occurs.…”

Section: Discussionmentioning

confidence: 99%

Survival Analysis of Lung Adenocarcinoma Patients with Exon 19 Del and 21 L858R Mutations Receiving EGFR-TKI Treatment

Prasetyo

Soeroso

Tarigan

et al. 2023

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Introduction: Patients with adenocarcinoma of the lung that have a common EGFR mutation, the Exon 19 Del mutation, survive better than those with the Exon 21 L858R mutation. This study examined whether there is a significant difference in prognosis between two common EGFR mutations, namely exon 19 Del and 21 L858R. This study compared OS (overall survival) and PFS (progression-free survival) in NSCLC patients with Exon 19 Del and Exon 21 L858R mutations who received EGFR-TKI targeted therapy at H. Adam Malik Hospital Medan. Methods: This analysis study used a retrospective cohort design to evaluate the OS and PFS of NSCLC patients who underwent EGFR-TKI precision medicine at H. Adam Hospital Malik Medan between January 1, 2017, and December 31, 2020 and also had Exon 19 Del and Exon 21 L858R alterations. Results: A total of 88 people were sampled. The majority of research subjects were male (60.2%). Median OS was eleven months (95 percent CI:9.594-12,406). According to the study's data, eight people (9.1%) survived until the study's ending. The median OS of Exon 19 Del Common Mutation was 11 months (95%CI 9,064-12,936). While Exon 21 L858R group had ten months (95%CI 4,546-15,454). The log-rank test identified no statistical difference in median OS between mutation types (p=0.562). Conclusion: The findings of this study revealed that subjects with Exon 19 Del mutations had a longer median OS and PFS than those with Exon 21 L858R variants. Nevertheless, there was no significant difference in median OS and PFS between study subjects with mutation of Exon 19 Del and Exon 21 L858R, which received the targeted medication.

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Section: Discussionmentioning

confidence: 99%

Survival Analysis of Lung Adenocarcinoma Patients with Exon 19 Del and 21 L858R Mutations Receiving EGFR-TKI Treatment

Prasetyo

Soeroso

Tarigan

et al. 2023

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“…From (11), it is clear that the survival function of the non-susceptible individuals is 1, whereas the proper survival function of those who are susceptible is a function of the frailty parameter, σ 2 . However, from (10), it follows that the cure fraction is dependent on the frailty parameter.…”

Section: Proposed Model: the Promotion Time Cure Model With Parametri...mentioning

confidence: 99%

“…where B is the cure status (or susceptibility) indicator, B = 1 indicates that an individual is susceptible to the event of interest (i.e., uncured) and B = 0 corresponds to individuals who are non-susceptible (i.e., cured). For the proposed model, (10) indicates that…”

Section: Maximum Likelihood Estimation Of the Ptc Model With Gamma Fr...mentioning

confidence: 99%

“…Recently, both mixture cure and promotion time cure (PTC) models have found applications in the credit risk environment (see e.g., [9][10][11][12]). Recognising that there is a proportion of defaulted loans that is not "susceptible" to a full recovery, de Oliveira and Louzada [5] modelled the recovery process of defaulted loans using a PTC model.…”

Section: Introductionmentioning

confidence: 99%

“…Notwithstanding the popularity of the CPH model, cure models have recently enjoyed increasing attention within the credit risk domain. This is particularly true for the modelling of the probability of default, which, in many high-quality loan portfolios, represents a rare event (see, e.g., [9][10][11]). Cure models, as all survival models, had their origin in medicine, where Boag [19] first suggested that there exists a certain proportion of cancer patients who will never experience the event of interest, i.e., they can be considered cured.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Modelling the Time to Write-Off of Non-Performing Loans Using a Promotion Time Cure Model with Parametric Frailty

et al. 2023

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Modelling the outcome after loan default is receiving increasing attention, and survival analysis is particularly suitable for this purpose due to the likely presence of censoring in the data. In this study, we suggest that the time to loan write-off may be influenced by latent competing risks, as well as by common, unobservable drivers, such as the state of the economy. We therefore expand on the promotion time cure model and include a parametric frailty parameter to account for common, unobservable factors and for possible observable covariates not included in the model. We opt for a parametric model due to its interpretability and analytical tractability, which are desirable properties in bank risk management. Both a gamma and inverse Gaussian frailty parameter are considered for the univariate case, and we also consider a shared frailty model. A Monte Carlo study demonstrates that the parameter estimation of the models is reliable, after which they are fitted to a real-world dataset in respect of large corporate loans in the US. The results show that a more flexible hazard function is possible by including a frailty parameter. Furthermore, the shared frailty model shows potential to capture dependence in write-off times within industry groups.

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Optimizing bank credit risk assessment models using big data analytics

Yang,

2024

Applied Mathematics and Nonlinear Sciences

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With the strong promotion of financial globalization and marketization and the rapid development of financial industry innovation, credit risk management is becoming an important work that banks need to pay more and more attention to. This paper focuses on the construction of the bank credit risk model to carry out research. First of all, according to the credit characteristics selection method, the optimal indicators are combined with the indicators for the correlation test of variables. On this basis, the sample data is calculated using the up-sampling method of SMOTE to complete the algorithm and sampling. After obtaining the data, data preprocessing is carried out using the MinMaxScaler scaler method, and the processed data is inputted into the improved momentum BP neural network to complete the credit risk assessment. Profiling with risk feature data, it is obtained that each feature is not a separate individual from the other. They are interdependent and connected and have obvious correlations. For example, there are 47 features in the dataset with sample missing ratios greater than 0.97, which is too high and indicates an invalid feature. The results of the study show that SMOTE’s up-sampling method and Momentum BP algorithm can quickly utilize big data to provide a more accurate decision basis for bank credit risk assessment.

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An overview of survival analysis with an application in the credit risk environment

Cited by 3 publications

References 27 publications

Survival Analysis of Lung Adenocarcinoma Patients with Exon 19 Del and 21 L858R Mutations Receiving EGFR-TKI Treatment

Survival Analysis of Lung Adenocarcinoma Patients with Exon 19 Del and 21 L858R Mutations Receiving EGFR-TKI Treatment

Modelling the Time to Write-Off of Non-Performing Loans Using a Promotion Time Cure Model with Parametric Frailty

Optimizing bank credit risk assessment models using big data analytics

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