Survival of HIV/AIDS patients is crucially dependent on comprehensive and targeted medical interventions such as supply of antiretroviral therapy and monitoring disease progression with CD4 T-cell counts. Statistical ...Survival of HIV/AIDS patients is crucially dependent on comprehensive and targeted medical interventions such as supply of antiretroviral therapy and monitoring disease progression with CD4 T-cell counts. Statistical modelling approaches are helpful towards this goal. This study aims at developing Bayesian joint models with assumed generalized error distribution (GED) for the longitudinal CD4 data and two accelerated failure time distributions, Lognormal and loglogistic, for the survival time of HIV/AIDS patients. Data are obtained from patients under antiretroviral therapy follow-up at Shashemene referral hospital during January 2006-January 2012 and at Bale Robe general hospital during January 2008-March 2015. The Bayesian joint models are defined through latent variables and association parameters and with specified non-informative prior distributions for the model parameters. Simulations are conducted using Gibbs sampler algorithm implemented in the WinBUGS software. The results of the analyses of the two different data sets show that distributions of measurement errors of the longitudinal CD4 variable follow the generalized error distribution with fatter tails than the normal distribution. The Bayesian joint GED loglogistic models fit better to the data sets compared to the lognormal cases. Findings reveal that patients’ health can be improved over time. Compared to the males, female patients gain more CD4 counts. Survival time of a patient is negatively affected by TB infection. Moreover, increase in number of opportunistic infection implies decline of CD4 counts. Patients’ age negatively affects the disease marker with no effects on survival time. Improving weight may improve survival time of patients. Bayesian joint models with GED and AFT distributions are found to be useful in modelling the longitudinal and survival processes. Thus we recommend the generalized error distributions for measurement errors of the longitudinal data under the Bayesian joint modelling. Further studies may investigate the m展开更多
This paper presents an application of the hazard model reliability analysis on wind generators, based on a condition monitoring system. The hazard model techniques are most widely used in the statistical analysis of t...This paper presents an application of the hazard model reliability analysis on wind generators, based on a condition monitoring system. The hazard model techniques are most widely used in the statistical analysis of the electric machine＇s lifetime data. The model can be utilized to perform appropriate maintenance decision-making based on the evaluation of the mean time to failures that occur on the wind generators due to high temperatures. The knowledge of the condition monitoring system is used to estimate the hazard failure, and survival rates, which allows the preventive maintenance approach to be performed accurately. A case study is presented to demonstrate the adequacy of the proposed method based on the condition monitoring data for two wind turbines. Such data are representative in the generator temperatures with respect to the expended operating hours of the selected wind turbines. In this context, the influence of the generator temperatures on the lifetime of the generators can be determined. The results of the study can be used to develop the predetermined maintenance program, which significantly reduces the maintenance and operation costs.展开更多
文摘Survival of HIV/AIDS patients is crucially dependent on comprehensive and targeted medical interventions such as supply of antiretroviral therapy and monitoring disease progression with CD4 T-cell counts. Statistical modelling approaches are helpful towards this goal. This study aims at developing Bayesian joint models with assumed generalized error distribution (GED) for the longitudinal CD4 data and two accelerated failure time distributions, Lognormal and loglogistic, for the survival time of HIV/AIDS patients. Data are obtained from patients under antiretroviral therapy follow-up at Shashemene referral hospital during January 2006-January 2012 and at Bale Robe general hospital during January 2008-March 2015. The Bayesian joint models are defined through latent variables and association parameters and with specified non-informative prior distributions for the model parameters. Simulations are conducted using Gibbs sampler algorithm implemented in the WinBUGS software. The results of the analyses of the two different data sets show that distributions of measurement errors of the longitudinal CD4 variable follow the generalized error distribution with fatter tails than the normal distribution. The Bayesian joint GED loglogistic models fit better to the data sets compared to the lognormal cases. Findings reveal that patients’ health can be improved over time. Compared to the males, female patients gain more CD4 counts. Survival time of a patient is negatively affected by TB infection. Moreover, increase in number of opportunistic infection implies decline of CD4 counts. Patients’ age negatively affects the disease marker with no effects on survival time. Improving weight may improve survival time of patients. Bayesian joint models with GED and AFT distributions are found to be useful in modelling the longitudinal and survival processes. Thus we recommend the generalized error distributions for measurement errors of the longitudinal data under the Bayesian joint modelling. Further studies may investigate the m
文摘This paper presents an application of the hazard model reliability analysis on wind generators, based on a condition monitoring system. The hazard model techniques are most widely used in the statistical analysis of the electric machine＇s lifetime data. The model can be utilized to perform appropriate maintenance decision-making based on the evaluation of the mean time to failures that occur on the wind generators due to high temperatures. The knowledge of the condition monitoring system is used to estimate the hazard failure, and survival rates, which allows the preventive maintenance approach to be performed accurately. A case study is presented to demonstrate the adequacy of the proposed method based on the condition monitoring data for two wind turbines. Such data are representative in the generator temperatures with respect to the expended operating hours of the selected wind turbines. In this context, the influence of the generator temperatures on the lifetime of the generators can be determined. The results of the study can be used to develop the predetermined maintenance program, which significantly reduces the maintenance and operation costs.
