scholarly journals K-means for shared frailty models

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Usha Govindarajulu ◽  
Sandeep Bedi
Keyword(s):  
Survival Analysis ◽  
Frailty Model ◽  
Real Data ◽  
Current Data ◽  
Frailty Models ◽  
Unmeasured Confounding ◽  
Real Dataset ◽  
Shared Frailty Model ◽  
Shared Frailty ◽  
Grouping Algorithm

Abstract Background The purpose of this research was to see how the k-means algorithm can be applied to survival analysis with single events per subject for defining groups, which can then be modeled in a shared frailty model to further allow the capturing the unmeasured confounding not already explained by the covariates in the model. Methods For this purpose we developed our own k-means survival grouping algorithm to handle this approach. We compared a regular shared frailty model with a regular grouping variable and a shared frailty model with a k-means grouping variable in simulations as well as analysis on a real dataset. Results We found that in both simulations as well as real data showed that our k-means clustering is no different than the typical frailty clustering even under different situations of varied case rates and censoring. It appeared our k-means algorithm could be a trustworthy mechanism of creating groups from data when no grouping term exists for including in a frailty term in a survival model or comparing to an existing grouping variable available in the current data to use in a frailty model.

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.

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 Modeling the Determinants of Time-to-Premarital Cohabitation among Women of Ethiopia: A Comparison of Various Parametric Shared Frailty Models

2021 ◽  
Author(s):  
Woldemariam Erkalo Gobena
Keyword(s):  
Risk Factors ◽  
Frailty Model ◽  
Frailty Models ◽  
Wealth Status ◽  
Premarital Cohabitation ◽  
Shared Frailty Model ◽  
Shared Frailty ◽  
Education Status ◽  
Potential Risk Factors ◽  
Clustering Effect

Abstract Background: Premarital cohabitation is defined as the state of living together and having a sexual relationship without being married. It has become more prevalent globally in recent decades. The main objective of this study was modeling the potential risk factors of time-to-premarital cohabitation among women of Ethiopia by using parametric shared frailty models where regional states of the women were used as a clustering effect in the models.Methods: The data source for the analysis was the 2016 EDHS data. The Gamma and Inverse-Gaussian shared frailty distributions with Exponential, Weibull, Log-logistic and Lognormal baseline models were employed to analyze risk factors associated with age at premarital cohabitation. All the fitted models were compared by using AIC values.Results: The median age of women at premarital cohabitation was 18 years. Based on AIC values, Log-logistic-Gamma shared frailty model has smallest AIC value among the models compared. The clustering effect was significant for modeling the determinants of time-to-premarital cohabitation dataset. The results showed that women’s education status, occupation, pregnancy and place of residence were found to be the most significant determinants of age at premarital cohabitation whereas wealth status and religion were not significant at 5% level.Conclusions: The Log-logistic-Gamma shared frailty model described the premarital cohabitation dataset better than other distributions used in this study. There is heterogeneity between the regions of women. Further studies should be conducted to identify other factors of age at premarital cohabitation of women in Ethiopia that were not included in this study.

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 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
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