scholarly journals Modeling Drivers’ Stopping Behaviors during Yellow Intervals at Intersections considering Group Heterogeneity

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Juan Li ◽  
Hui Zhang ◽  
Yanru Zhang ◽  
Xuan Zhang
Keyword(s):  
Goodness Of Fit ◽  
Failure Time ◽  
Stopping Time ◽  
Frailty Model ◽  
Survival Models ◽  
Traffic Incidents ◽  
Aft Model ◽  
Homogeneity Assumption ◽  
Group Heterogeneity ◽  
Shared Frailty

Stopping behavior during yellow intervals is one of the critical driver behaviors correlated with intersection safety. As the main index of stopping behavior, stopping time is typically described by Accelerated Failure Time (AFT) model. In this study, the comparison of survival curves of stopping time confirms the existence of group specific effects on drivers. However, the AFT model is developed based on the homogeneity assumption. To overcome this drawback, shared frailty survival models are developed for stopping time analysis, which consider the group heterogeneity of drivers. The results show that log-logistic based frailty model with age as a grouping variable has the best goodness of fit and prediction accuracy. Analysis of the models’ parameters indicates that phone status, maximum deceleration, vehicles’ speed, and the distance to stopping line at the onset of the yellow signal have significant impacts on stopping time. Additionally, heterogeneity analysis illustrates that young, middle-aged, and female drivers are more likely to brake harshly and stop past the stop line, which may block the intersection. Furthermore, drivers, who are more familiar with traffic environments, are more possible to make reasonable stopping decisions approaching intersections. The results can be utilized by traffic authorities to implement road safety strategies, which will help reduce traffic incidents caused by improper stopping behavior at intersections.

scholarly journals Comparison of Cox Proportional Hazards Model and Lognormal Accelerated Failure Time Model: Application in Time to Event Analysis of Acute Liver Failure Patients in India

2019 ◽  
Vol 3 ◽  
pp. 21-40
Author(s):  
Shankar Prasad Khanal ◽  
V. Sreenivas ◽  
Subrat K. Acharya
Keyword(s):  
Goodness Of Fit ◽  
Proportional Hazards ◽  
Failure Time ◽  
Model Performance ◽  
Cox Proportional Hazards ◽  
Accelerated Failure Time ◽  
Validation Data ◽  
Data Set ◽  
Final Model ◽  
Aft Model

Background: Different survival analysis techniques such as nonparametric, semi-parametric, parametric Accelerated Failure Time (AFT) models have been generally applied to analyze time to event data. In order to identify the prognostic factors for survival of Acute Liver Failure (ALF) patients, previous studies applied Cox Proportional hazards (CPH) model, Lognormal AFT and Log-Logistic AFT model satisfying respective model’s assumptions and goodness of fit of each model. However, comparison of CPH model and AFT model has not been reported so far for ALF data with short follow up time. Objective: To compare CPH model and Lognormal AFT model based on different parameters for assessing the model performance and prospective validation of the finally selected model. Materials and Methods: Altogether 1099 ALF patients’ data from liver clinic of All India Institute of Medical Sciences, New Delhi India were analyzed based on the retrospective cohort study design. For validating the final model, a separate data set of 138 ALF patients from the same clinic was used. CPH model and Lognormal model’s performance was assessed through selection of variables in the final model, R2 type statistic, goodness of fit of the model, visual assessment of Cox-Snell’s residuals plot and robustness of the model. The prospective validation of the over scored CPH model was done by comparing overall survival, regression coefficients, observed and predicted survival curves between original and validation data set. Results: It is found that 60% of variation in the partial log-likelihood is explained by the CPH model whereas 39% of variation in full log-likelihood is explained by Lognormal AFT model. Cox-Snell residuals plot for CPH model seems less deviated from the line of ideal fit, replications of variables measured through bootstrapping resampling technique in CPH model are on the higher side, model predicted and observed survival curves in each risk stratum were closer than that of Lognormal model. The survival experience of original data and validation data set for CPH model does not seem to be very different (p = 0.07) at 5% level of significance. Conclusion: Both CPH and Lognormal AFT model are found well fitted and can be applied either of them for this ALF data. While comparing the model performance, the CPH model for the identification of prognostic factors for the survival of ALF patients is found comparatively better.

scholarly journals Random effect bivariate survival models and stochastic comparisons

2010 ◽  
Vol 47 (02) ◽  
pp. 426-440 ◽  
Author(s):  
Ramesh C. Gupta ◽  
Rameshwar D. Gupta
Keyword(s):  
Random Effect ◽  
Random Effect Model ◽  
Frailty Model ◽  
Survival Models ◽  
Survival Functions ◽  
Stochastic Comparisons ◽  
Effect Model ◽  
Shared Frailty Model ◽  
Shared Frailty ◽  
The Relationship

In this paper we propose a general bivariate random effect model with special emphasis on frailty models and environmental effect models, and present some stochastic comparisons. The relationship between the conditional and the unconditional hazard gradients are derived and some examples are provided. We investigate how the well-known stochastic orderings between the distributions of two frailties translate into the orderings between the corresponding survival functions. These results are used to obtain the properties of the bivariate multiplicative model and the shared frailty model.

scholarly journals Random effect bivariate survival models and stochastic comparisons

2010 ◽  
Vol 47 (2) ◽  
pp. 426-440 ◽  
Author(s):  
Ramesh C. Gupta ◽  
Rameshwar D. Gupta
Keyword(s):  
Random Effect ◽  
Random Effect Model ◽  
Frailty Model ◽  
Survival Models ◽  
Survival Functions ◽  
Stochastic Comparisons ◽  
Effect Model ◽  
Shared Frailty Model ◽  
Shared Frailty ◽  
The Relationship

In this paper we propose a general bivariate random effect model with special emphasis on frailty models and environmental effect models, and present some stochastic comparisons. The relationship between the conditional and the unconditional hazard gradients are derived and some examples are provided. We investigate how the well-known stochastic orderings between the distributions of two frailties translate into the orderings between the corresponding survival functions. These results are used to obtain the properties of the bivariate multiplicative model and the shared frailty model.

scholarly journals Dependent Dirichlet Processes for Analysis of a Generalized Shared Frailty Model

2021 ◽  
Author(s):  
Chong Zhong ◽  
Zhihua Ma ◽  
Junshan Shen ◽  
Catherine Liu
Keyword(s):  
Survival Analysis ◽  
Dirichlet Process ◽  
Survival Function ◽  
Frailty Model ◽  
Survival Models ◽  
Bayesian Paradigm ◽  
Posterior Sampling ◽  
Posterior Inference ◽  
Shared Frailty Model ◽  
Shared Frailty

Bayesian paradigm takes advantage of well-fitting complicated survival models and feasible computing in survival analysis owing to the superiority in tackling the complex censoring scheme, compared with the frequentist paradigm. In this chapter, we aim to display the latest tendency in Bayesian computing, in the sense of automating the posterior sampling, through a Bayesian analysis of survival modeling for multivariate survival outcomes with the complicated data structure. Motivated by relaxing the strong assumption of proportionality and the restriction of a common baseline population, we propose a generalized shared frailty model which includes both parametric and nonparametric frailty random effects to incorporate both treatment-wise and temporal variation for multiple events. We develop a survival-function version of the ANOVA dependent Dirichlet process to model the dependency among the baseline survival functions. The posterior sampling is implemented by the No-U-Turn sampler in Stan, a contemporary Bayesian computing tool, automatically. The proposed model is validated by analysis of the bladder cancer recurrences data. The estimation is consistent with existing results. Our model and Bayesian inference provide evidence that the Bayesian paradigm fosters complex modeling and feasible computing in survival analysis, and Stan relaxes the posterior inference.

scholarly journals Training Load and Baseline Characteristics Associated With New Injury/Pain Within an Endurance Sporting Population: A Prospective Study

2019 ◽  
Vol 14 (5) ◽  
pp. 590-597 ◽  
Author(s):  
Richard Johnston ◽  
Roisin Cahalan ◽  
Laura Bonnett ◽  
Matthew Maguire ◽  
Alan Nevill ◽  
...  
Keyword(s):  
Confidence Interval ◽  
Frailty Model ◽  
Training Load ◽  
Moving Averages ◽  
Shared Frailty Model ◽  
Shared Frailty ◽  
History Of ◽  
Baseline Characteristics ◽  
A Prospective Study ◽  
Exponentially Weighted

Purpose: To determine the association between training-load (TL) factors, baseline characteristics, and new injury and/or pain (IP) risk in an endurance sporting population (ESP). Methods: Ninety-five ESP participants from running, triathlon, swimming, cycling, and rowing disciplines initially completed a questionnaire capturing baseline characteristics. TL and IP data were submitted weekly over a 52-wk study period. Cumulative TL factors, acute:chronic workload ratios, and exponentially weighted moving averages were calculated. A shared frailty model was used to explore time to new IP and association to TL factors and baseline characteristics. Results: 92.6% of the ESP completed all 52 wk of TL and IP data. The following factors were associated with the lowest risk of a new IP episode: (a) a low to moderate 7-d lag exponentially weighted moving averages (0.8–1.3: hazard ratio [HR] = 1.21; 95% confidence interval [CI], 1.01–1.44; P = .04); (b) a low to moderate 7-d lag weekly TL (1200–1700 AU: HR = 1.38; 95% CI, 1.15–1.65; P < .001); (c) a moderate to high 14-d lag 4-weekly cumulative TL (5200–8000 AU: HR = 0.33; 95% CI, 0.21–0.50; P < .001); and (d) a low number of previous IP episodes in the preceding 12 mo (1 previous IP episode: HR = 1.11; 95% CI, 1.04–1.17; P = .04). Conclusions: To minimize new IP risk, an ESP should avoid high spikes in acute TL while maintaining moderate to high chronic TLs. A history of previous IP should be considered when prescribing TLs. The demonstration of a lag between a TL factor and its impact on new IP risk may have important implications for future ESP TL analysis.

scholarly journals A High-Stakes Approach to Response Time Effort in Low-Stakes Assessment

2021 ◽  
Vol 7 (4) ◽  
pp. 571-586
Author(s):  
Munevver Ilgun
Keyword(s):  
Response Time ◽  
Failure Time ◽  
Response Times ◽  
Survival Models ◽  
Accelerated Failure Time ◽  
Data Sets ◽  
High Stakes ◽  
Problem Solving Skills ◽  
Educational Stakeholders ◽  
Accelerated Failure Time Models

<p style="text-align: justify;">Response times are one of the important sources that provide information about the performance of individuals during a test process. The main purpose of this study is to show that survival models can be used in educational data. Accordingly, data sets of items measuring literacy, numeracy and problem-solving skills of the countries participating in Round 3 of the Programme for the International Assessment of Adult Competencies were used. Accelerated failure time models have been analyzed for each country and domain.  As a result of the analysis of the models in which various covariates are included as independent variables, and response time for giving correct answers is included as a dependent variable, it was found the associations between the covariates and response time for giving correct answers were concluded to vary from one domain to another or from one country to another. The results obtained from the present study have provided the educational stakeholders and practitioners with valuable information.</p>

scholarly journals A time-varying shared frailty model with application to infectious diseases

2014 ◽  
Vol 8 (1) ◽  
pp. 430-447 ◽  
Author(s):  
Doyo G. Enki ◽  
Angela Noufaily ◽  
C. Paddy Farrington
Keyword(s):  
Infectious Diseases ◽  
Frailty Model ◽  
Time Varying ◽  
Shared Frailty Model ◽  
Shared Frailty

The impact of inventory productivity on new venture survival

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yu Lin ◽  
Jiannan Wang ◽  
Yingjie Shi
Keyword(s):  
Financial Constraints ◽  
Failure Time ◽  
Performance Indicator ◽  
Marginal Effect ◽  
Industrial Firms ◽  
Content Type ◽  
Aft Model ◽  
Annual Survey ◽  
Inherent Nature ◽  
The Impact

PurposeThis paper explores the relationship between inventory productivity and the likelihood of venture survival and then examines how financial constraints moderate the inventory productivity–survival linkage.Design/methodology/approachAccelerated failure time (AFT) model is employed to study the link between inventory productivity and venture survival by using small- and medium-sized enterprise (SME) data from Chinese Annual Survey of Industrial Firms (CASIF) database over the period 1999–2007.FindingsThe paper demonstrates a converse U-curve relation between inventory productivity and venture survival. Additionally, financial constraints as the moderator weaken the marginal effect of inventory productivity on venture survival.Practical implicationsManagers should pay more attention to the important inventory performance indicator: inventory productivity. In the context of prominent financing difficulties, managers should be rapid to adjust the competitive strategy and optimize the internal production process according to the inherent nature of risks in a friction environment, and thus generate resources that enterprises cannot raise in the financial market.Originality/valueThis study may be the first to practically investigate the role of inventory productivity on venture survival and the moderating effect of financing constraints on this relationship. It adds to abundant articles as regards the interface between operation management and venture survival by exploring how financial constraints moderate the inventory productivity–survival linkage.

Determinants of Time-to-recovery from Hypertension using a Comparison of Various Parametric Shared Frailty Models: In Case of Felege Hiwot Refral Hospital.

2021 ◽  
Author(s):  
Nigist Mulu ◽  
Yeshambel Kindu ◽  
Abay Kassie
Keyword(s):  
Survival Time ◽  
Blood Urea Nitrogen ◽  
Normal Condition ◽  
Recovery Time ◽  
Frailty Model ◽  
Frailty Models ◽  
Inverse Gaussian ◽  
Urea Nitrogen ◽  
Shared Frailty Model ◽  
Shared Frailty

Abstract Background: Hypertension is a major public health problem that is responsible for morbidity and mortality. In Ethiopia hypertension is becoming a double burden due to urbanization. The study aimed to identify factors that affect time-to-recovery from hypertension at Felege Hiwot Referral Hospital. Retrospective study design was used at FHRH. Methods: The data was collected in patient’s chart from September 2016 to January 2018. Kaplan-Meier survival estimate and Log-Rank test were used to compare the survival time. The AFT and parametric shared frailty models were employed to identify factors associated with the recovery time of hypertension patients. All the fitted models were compared by using AIC and BIC. Results: Eighty one percent of sampled patients were recovered to normal condition and nineteen percent of patients were censored observations. The median survival time of hypertensive patients to attain normal condition was 13 months. Weibull- inverse Gaussian shared frailty model was found to be the best model for predicting recovery time of hypertension patients. The unobserved heterogeneity in residences as estimated by the Weibull-Inverse Gaussian shared frailty model was θ=0.385 (p-value=0.00). Conclusion: The final model showed that age, systolic blood pressure, related disease, creantine, blood urea nitrogen and the interaction between blood urea nitrogen and age were the determinants factors of recovery status of patients at 5% level of significance. The result showed that patients creantine >1.5 Mg/dl compared to creantine ≤1.5 Mg/dl and SBP were prolonged the recovery time of patients whereas patients having kidney disease, other disease and had no any disease compared to diabetic patients and the interaction BUN and age were shorten recovery status of hypertension patients.

Sign in / Sign up

Export Citation Format

Share Document