Concordance measures in shared frailty models: application to clustered data in cancer prognosis

2013 ◽  
Vol 32 (27) ◽  
pp. 4803-4820 ◽  
Author(s):  
Audrey Mauguen ◽  
Sandra Collette ◽  
Jean-Pierre Pignon ◽  
Virginie Rondeau
Keyword(s):  
Clustered Data ◽  
Frailty Models ◽  
Cancer Prognosis ◽  
Shared Frailty ◽  
Concordance Measures

Interpretation of concordance measures for clustered data

2014 ◽  
Vol 33 (4) ◽  
pp. 714-716 ◽  
Author(s):  
David van Klaveren ◽  
Ewout W. Steyerberg ◽  
Yvonne Vergouwe
Keyword(s):  
Clustered Data ◽  
Concordance Measures

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.

One-sided tests in shared frailty models

Test ◽  
2007 ◽  
Vol 17 (1) ◽  
pp. 69-82 ◽  
Author(s):  
Gerda Claeskens ◽  
Rosemary Nguti ◽  
Paul Janssen
Keyword(s):  
Frailty Models ◽  
Shared Frailty

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.

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