Modeling the influence of mobile phone use distraction on pedestrian reaction times to green signals: A multilevel mixed-effects parametric survival model

2021 ◽  
Vol 81 ◽  
pp. 115-129
Author(s):  
Yan Liu ◽  
Rushdi Alsaleh ◽  
Tarek Sayed
Keyword(s):  
Mobile Phone ◽  
Reaction Times ◽  
Mixed Effects ◽  
Survival Model ◽  
Mobile Phone Use ◽  
Parametric Survival Model ◽  
Parametric Survival

scholarly journals Evaluation of factors related to the survival of hospitalized patients with COVID-19: Survival analysis with frailty approach

2021 ◽  
pp. 1-16
Author(s):  
Parvin Sarbakhsh ◽  
Saba Ghaffary ◽  
Elnaz Shaseb
Keyword(s):  
Unobserved Heterogeneity ◽  
Multivariable Analysis ◽  
Complete Recovery ◽  
Important Variable ◽  
Hospitalized Patients ◽  
Survival Model ◽  
P Value ◽  
Factors Affecting ◽  
Parametric Survival Model ◽  
Parametric Survival

Abstract Introduction: Considering that covid-19 is an emerging disease and results in very different outcomes-from complete recovery to death, it is important to determine the factors affecting the survival of patients. Given the lack of knowledge about effective factors and the existence of differences in the outcome of individuals with similar values of the observed covariates, this study aimed to investigate the factors affecting the survival of patients with COVID-19 by the parametric survival model with the frailty approach. Methods: The data of 139 patients with COVID-19 hospitalized in Imam Reza Hospital in Tabriz were analyzed by the Gompertz survival model with gamma frailty effect. At first, variables with p-value<.1 in univariable analysis were included in the multivariable analysis, and then the stepwise method was used for variable selection. Results: Diabetes mellitus (p-value =.021) was significantly related to the survival of hospitalized patients. The rest of the investigated variables were not significant. The frailty effect was significant (p-value=.019). Conclusion: In the investigated sample of patients with covid19, diabetes was an important variable related to patient survival. Also, the significant frailty effect indicates the existence of unobserved heterogeneity that cause individuals with a similar value of the observed covariates to have different survival distributions.

scholarly journals Bayesian variable selection for parametric survival model with applications to cancer omics data

2018 ◽  
Vol 12 (1) ◽  
Author(s):  
Weiwei Duan ◽  
Ruyang Zhang ◽  
Yang Zhao ◽  
Sipeng Shen ◽  
Yongyue Wei ◽  
...  
Keyword(s):  
Variable Selection ◽  
Bayesian Variable Selection ◽  
Survival Model ◽  
Omics Data ◽  
Parametric Survival Model ◽  
Selection For ◽  
Parametric Survival
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