scholarly journals Survival Following Road Traffic Accidents in a Level-I Trauma Center, Parametric versus Semi-Parametric Survival Models

2021 ◽  
Keyword(s):  
Multivariate Analysis ◽  
Survival Analysis ◽  
Statistical Models ◽  
Trauma Center ◽  
Proportional Hazards ◽  
Road Traffic ◽  
Weibull Model ◽  
Parametric Models ◽  
Level I ◽  
Parametric Survival

Background: Simulation studies present an important statistical tool to investigate the performance, properties, and adequacy of statistical models in pre-specified situations. The proportional hazards model of survival analysis is one of the most important statistical models in medical studies. This study aimed to investigate the underlying one-month survival of road traffic accident (RTA) victims in a Level 1 Trauma Center in Iran using parametric and semi-parametric survival analysis models from the viewpoint of post-crash care-provider in 2017. Materials and Methods: This retrospective cohort study (restudy) was conducted at Level-I Trauma Center of Shiraz, Iran, from January to December 2017. Considering the fact that certain covariates acting on survival may take a non-homogenous risk pattern leading to the violation of proportional hazards assumption in Cox-PH, the parametric survival modeling was employed to inspect the multiplicative effect of all covariates on the hazard. Distributions of choice were Exponential, Weibull and Lognormal. Parameters were estimated using the Akaike Results: Survival analysis was conducted on 8,621 individuals for whom the length of stay (observation period) was between 1 and 89 days. In total, 141 death occurred during this time. The log-rank test revealed inequality of survival functions across various categories of age, injury mechanism, injured body region, injury severity score, and nosocomial infections. Although the risk level in the Cox model is almost the same as that in the results of the parametric models, the Weibull model in the multivariate analysis yields better results, according to the Akaike criterion. Conclusion: In multivariate analysis, parametric models were more efficient than other models. Some results were similar in both parametric and semi-parametric models. In general, parametric models and among them the Weibull model was more efficient than other models.

scholarly journals A bivariate power generalized Weibull distribution: A flexible parametric model for survival analysis

2019 ◽  
Vol 29 (8) ◽  
pp. 2295-2306 ◽  
Author(s):  
MC Jones ◽  
Angela Noufaily ◽  
Kevin Burke
Keyword(s):  
Survival Analysis ◽  
Weibull Distribution ◽  
Survival Data ◽  
Frailty Model ◽  
Weibull Model ◽  
Flexible Parametric Model ◽  
Shared Frailty Model ◽  
Shared Frailty ◽  
Power Variance ◽  
Parametric Survival

We are concerned with the flexible parametric analysis of bivariate survival data. Elsewhere, we argued in favour of an adapted form of the ‘power generalized Weibull’ distribution as an attractive vehicle for univariate parametric survival analysis. Here, we additionally observe a frailty relationship between a power generalized Weibull distribution with one value of the parameter which controls distributional choice within the family and a power generalized Weibull distribution with a smaller value of that parameter. We exploit this relationship to propose a bivariate shared frailty model with power generalized Weibull marginal distributions linked by the BB9 or ‘power variance function’ copula, then change it to have adapted power generalized Weibull marginals in the obvious way. The particular choice of copula is, therefore, natural in the current context, and the corresponding bivariate adapted power generalized Weibull model a novel combination of pre-existing components. We provide a number of theoretical properties of the models. We also show the potential of the bivariate adapted power generalized Weibull model for practical work via an illustrative example involving a well-known retinopathy dataset, for which the analysis proves to be straightforward to implement and informative in its outcomes.

scholarly journals Prehospital Intervals and In-Hospital Trauma Mortality: A Retrospective Study from a Level I Trauma Center

2020 ◽  
Vol 35 (5) ◽  
pp. 508-515
Author(s):  
Hassan Al-Thani ◽  
Ahammed Mekkodathil ◽  
Attila J. Hertelendy ◽  
Tim Frazier ◽  
Gregory R. Ciottone ◽  
...  
Keyword(s):  
Trauma Center ◽  
Injury Severity ◽  
Road Traffic ◽  
Trauma Patients ◽  
Golden Hour ◽  
Chest Injuries ◽  
Life Saving ◽  
Level I ◽  
Prehospital Time ◽  
The Impact

AbstractBackground:The increase in mortality and total prehospital time (TPT) seen in Qatar appear to be realistic. However, existing reports on the influence of TPT on mortality in trauma patients are conflicting. This study aimed to explore the impact of prehospital time on the in-hospital outcomes.Methods:A retrospective analysis of data on patients transferred alive by Emergency Medical Services (EMS) and admitted to Hamad Trauma Center (HTC) of Hamad General Hospital (HGH; Doha, Qatar) from June 2017 through May 2018 was conducted. This study was centered on the National Trauma Registry database. Patients were categorized based on the trauma triage activation and prehospital intervals, and comparative analysis was performed.Results:A total of 1,455 patients were included, of which nearly one-quarter of patients required urgent and life-saving care at a trauma center (T1 activations). The overall TPT was 70 minutes and the on-scene time (OST) was 24 minutes. When compared to T2 activations, T1 patients were more likely to have been involved in road traffic injuries (RTIs); experienced head and chest injuries; presented with higher Injury Severity Score (ISS: median = 22); and had prolonged OST (27 minutes) and reduced TPT (65 minutes; P = .001). Prolonged OST was found to be associated with higher mortality in T1 patients, whereas TPT was not associated.Conclusions:In-hospital mortality was independent of TPT but associated with longer OST in severely injured patients. The survival benefit may extend beyond the golden hour and may depend on the injury characteristics, prehospital, and in-hospital settings.

Establishment, Functioning, Challenges, and One-Year Report of Uttar Pradesh’s First Apex Trauma Center

2019 ◽  
Vol 16 (02/03) ◽  
pp. 099-105
Author(s):  
Mallikarjun Gunjiganvi ◽  
Siddharth Rai ◽  
Rupali Awale ◽  
Amit Agarwal
Keyword(s):  
Trauma Care ◽  
Trauma Center ◽  
Spinal Cord Injuries ◽  
Road Traffic ◽  
Quality Care ◽  
Public Health Problem ◽  
Major Public Health Problem ◽  
Medical Sciences ◽  
Provide Quality Care ◽  
Level I

AbstractTrauma is a major public health problem across the world with significant morbidity and mortality. Broadly, it is a disease of middle-aged population and is assuming the status of an epidemic in the 21st century. Road traffic injuries are most common followed by railway injuries, industrial, farming, and domestic injuries, and many others in low- and middle-income countries. Severe traumatic brain injuries are the major proportion with concern for long-term cognitive impairment and high spinal cord injuries due to complete dependence. There is no comprehensive trauma care system covering all geography in India at present. The Government of India (GOI), in 2006, established Jai Prakash Narayan Apex Trauma Center, which is run by All India Institute of Medical Sciences at New Delhi as an apex center to provide quality care, training, research, and registry development. It acts as a role model center for the establishment of new centers and helps in upgradation of existing hospitals to provide quality care trauma services. To curb this epidemic of trauma, GOI envisioned National Trauma Care program during the 11th and 12th Five-Year Plans to strengthen the emergency facilities in government hospitals. Many new centers are coming up with various levels of trauma care across the country. Here we discuss the establishment, resources, initial challenges, trauma burden, and a year of report card of the Uttar Pradesh’s first Level I Apex Trauma Center of Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, established with a vision of providing state of the art Level I trauma care to the injured victims.

scholarly journals A Review of Survival Analysis Methods Used in NICE Technology Appraisals of Cancer Treatments: Consistency, Limitations, and Areas for Improvement

2019 ◽  
Vol 39 (8) ◽  
pp. 899-909 ◽  
Author(s):  
Helen Bell Gorrod ◽  
Ben Kearns ◽  
John Stevens ◽  
Praveen Thokala ◽  
Alexander Labeit ◽  
...  
Keyword(s):  
Survival Analysis ◽  
Model Selection ◽  
Proportional Hazards ◽  
Survival Function ◽  
Progression Free Survival ◽  
Parametric Models ◽  
Survival Functions ◽  
Cancer Treatments ◽  
Hazard Functions ◽  
Support Unit

Objectives. In June 2011, the National Institute for Health and Care Excellence (NICE) Decision Support Unit published a Technical Support Document (TSD) providing recommendations on survival analysis for NICE technology appraisals (TAs). Survival analysis outputs are influential inputs into economic models estimating the cost-effectiveness of new cancer treatments. Hence, it is important that systematic and justifiable model selection approaches are used. This study investigates the extent to which the TSD recommendations have been followed since its publication. Methods. We reviewed NICE cancer TAs completed between July 2011 and July 2017. Information on survival analyses undertaken and associated critiques for overall survival (OS) and progression-free survival were extracted from the company submissions, Evidence Review Group (ERG) reports, and final appraisal determination documents. Results. Information was extracted from 58 TAs. Only 4 (7%) followed all TSD recommendations for OS outcomes. The vast majority (91%) compared a range of common parametric models and assessed their fit to the data (86%). Only a minority of TAs included an assessment of the shape of the hazard function (38%) or proportional hazards assumption (40%). Validation of the extrapolated portion of the survival function using external data was attempted in a minority of TAs (40%). Extrapolated survival functions were frequently criticized by ERGs (71%). Conclusions. Survival analysis within NICE TAs remains suboptimal, despite publication of the TSD. Model selection is not undertaken in a systematic way, resulting in inconsistencies between TAs. More attention needs to be given to assessing hazard functions and validation of extrapolated survival functions. Novel methods not described in the TSD have been used, particularly in the context of immuno-oncology, suggesting that an updated TSD may be of value.

Survival Analysis

2009 ◽  
Author(s):  
Jonathan Golub
Keyword(s):  
Time Series ◽  
Survival Analysis ◽  
Proportional Hazards ◽  
Temporal Information ◽  
Parametric Models ◽  
Cox Models ◽  
Standard Practice ◽  
Baseline Hazard ◽  
Political Processes ◽  
Proportional Hazards Assumption

This article provides a discussion of survival analysis that presents another way to incorporate temporal information into analysis in ways that give advantages similar to those from using time series. It describes the main choices researchers face when conducting survival analysis and offers a set of methodological steps that should become standard practice. After introducing the basic terminology, it shows that there is little to lose and much to gain by employing Cox models instead of parametric models. Cox models are superior to parametric models in three main respects: they provide more reliable treatment of the baseline hazard and superior handling of the proportional hazards assumption, and they are the best for handling tied data. Moreover, the illusory benefits of parametric models are presented. The greater use of Cox models enables researchers to elicit more useful information from their data, and allows for more reliable substantive inferences about important political processes.

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.

scholarly journals Prehospital time and mortality in polytrauma patients: a retrospective analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
E. Berkeveld ◽  
Z. Popal ◽  
P. Schober ◽  
W. P. Zuidema ◽  
F. W. Bloemers ◽  
...  
Keyword(s):  
Patient Outcome ◽  
Emergency Medical Services ◽  
Trauma Center ◽  
Total Mortality ◽  
Medical Services ◽  
Severely Injured ◽  
Emergency Medical ◽  
Polytrauma Patients ◽  
Level I ◽  
Prehospital Time

Abstract Background The time from injury to treatment is considered as one of the major determinants for patient outcome after trauma. Previous studies already attempted to investigate the correlation between prehospital time and trauma patient outcome. However, the outcome for severely injured patients is not clear yet, as little data is available from prehospital systems with both Emergency Medical Services (EMS) and physician staffed Helicopter Emergency Medical Services (HEMS). Therefore, the aim was to investigate the association between prehospital time and mortality in polytrauma patients in a Dutch level I trauma center. Methods A retrospective study was performed using data derived from the Dutch trauma registry of the National Network for Acute Care from Amsterdam UMC location VUmc over a 2-year period. Severely injured polytrauma patients (Injury Severity Score (ISS) ≥ 16), who were treated on-scene by EMS or both EMS and HEMS and transported to our level I trauma center, were included. Patient characteristics, prehospital time, comorbidity, mechanism of injury, type of injury, HEMS assistance, prehospital Glasgow Coma Score and ISS were analyzed using logistic regression analysis. The outcome measure was in-hospital mortality. Results In total, 342 polytrauma patients were included in the analysis. The total mortality rate was 25.7% (n = 88). Similar mean prehospital times were found between the surviving and non-surviving patient groups, 45.3 min (SD 14.4) and 44.9 min (SD 13.2) respectively (p = 0.819). The confounder-adjusted analysis revealed no significant association between prehospital time and mortality (p = 0.156). Conclusion This analysis found no association between prehospital time and mortality in polytrauma patients. Future research is recommended to explore factors of influence on prehospital time and mortality.

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