1. Risk assessment in liver transplantation patients: A shared frailty parametric approach 
Dilip C. Nath, Atanu Bhattacharjee, Ramesh K. Vishwakarma 
Clinical Epidemiology and Global Health 
Volume 4, Issue 1, Pages 1-15 (March 2016)
DOI: /j.cegh
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2. Fig. 1 Kaplan–Meier plot for Therapy.
Clinical Epidemiology and Global Health 2016 4, 1-15DOI: ( /j.cegh )
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3. Fig. 2 Kaplan–Meier plot for Gender.
Clinical Epidemiology and Global Health 2016 4, 1-15DOI: ( /j.cegh )
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4. Fig. 3 Hazard plot for Therapy.
Clinical Epidemiology and Global Health 2016 4, 1-15DOI: ( /j.cegh )
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5. Fig. 4 Hazard plot for Gender.
Clinical Epidemiology and Global Health 2016 4, 1-15DOI: ( /j.cegh )
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6. Fig. 5
Prediction of frailties for the liver transplantation data, as given by the parametric gamma frailty with Exponential baseline hazard.
Clinical Epidemiology and Global Health 2016 4, 1-15DOI: ( /j.cegh )
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7. Fig. 6
Prediction of frailties for the liver transplantation data, as given by the parametric inverse Gaussian frailty with Weibull baseline hazard.
Clinical Epidemiology and Global Health 2016 4, 1-15DOI: ( /j.cegh )
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8. Fig. 7
Prediction of frailties for the liver transplantation data, as given by the parametric inverse Gaussian frailty with Exponential baseline hazard.
Clinical Epidemiology and Global Health 2016 4, 1-15DOI: ( /j.cegh )
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9. Fig. 8
Prediction of frailties for the liver transplantation data, as given by the parametric positive stable frailty with Weibull baseline hazard.
Clinical Epidemiology and Global Health 2016 4, 1-15DOI: ( /j.cegh )
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10. Fig. 9
Prediction of frailties for the liver transplantation data, as given by the parametric positive stable frailty with Exponential baseline hazard.
Clinical Epidemiology and Global Health 2016 4, 1-15DOI: ( /j.cegh )
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11. Fig. 10
Prediction of frailties for the liver transplantation data, as given by the parametric lognormal frailty with Exponential baseline hazard.
Clinical Epidemiology and Global Health 2016 4, 1-15DOI: ( /j.cegh )
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12. Fig. 11
Comparing the five models with respect to AIC and BIC values when covariate is considered as Therapy.
Clinical Epidemiology and Global Health 2016 4, 1-15DOI: ( /j.cegh )
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13. Fig. 12
Comparing the five models with respect to AIC and BIC values when covariate is considered as Therapy and Gender.
Clinical Epidemiology and Global Health 2016 4, 1-15DOI: ( /j.cegh )
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14. Fig. 13
Comparing the five models with respect to AIC and BIC values when covariate is considered as Therapy, Gender and MELD.
Clinical Epidemiology and Global Health 2016 4, 1-15DOI: ( /j.cegh )
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15. Fig. 14
Comparing the five models with respect to AIC and BIC values when covariate is considered as Therapy, Gender, MELD and Triglycerides.
Clinical Epidemiology and Global Health 2016 4, 1-15DOI: ( /j.cegh )
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16. Fig. 15
Comparing the five models with respect to AIC and BIC values when covariate is considered as Therapy, Gender, MELD, Centre and Triglycerides.
Clinical Epidemiology and Global Health 2016 4, 1-15DOI: ( /j.cegh )
Copyright © 2015 INDIACLEN Terms and Conditions