Bayesian Parametric Modeling of Time to Tuberculosis Co-Infection of HIV/AIDS Patients Under Antiretroviral Therapy Treatment at Jimma University Medical Center, Ethiopia

Abstract Background: Tuberculosis is the most common opportunistic infection among HIV/AIDS patients, including those following Antiretroviral Therapy treatment. The risk of Tuberculosis infection is higher in people living with HIV/AIDS than in people who are free from HIV/AIDS. Many studies focused on prevalence and determinants of Tuberculosis in HIV/AIDS patients without taking into account the censoring aspects of the time to event data. Therefore, this study was undertaken with aim to model time to Tuberculosis co-infection of HIV/AIDS patients following Antiretroviral Therapy treatment using Bayesian parametric survival models.Methods: A data of a retrospective cohort of HIV/AIDS patients under Antiretroviral Therapy treatment follow-up from January 2016 to December 2020 until Tuberculosis was clinically diagnosed or until the end of the study was collected from Antiretroviral Therapy treatment center of Jimma University Medical Center, Ethiopia. In order to identify the risk factors which have association with Tuberculosis co-infection survival time, Bayesian parametric Accelerated failure time survival models were fitted to the data using Integrated Nested Laplace Approximation methodology.Results: About 26.37% of the study subjects had been co-infected with tuberculosis during the study period. Among the parametric Accelerated failure time models, the Bayesian log-logistic Accelerated failure time model was found to be the best fitting model for the data.Conclusions: Tuberculosis co-infection survival time was significantly associated with place of residence, smoking, drinking alcohol, family size, WHO clinical stages, functional status, CD4 count, BMI and hemoglobin level. The finding of this study provide timely information on the risk factors associated with TB co-infection survival time for healthy policy makers and planners.

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Bayesian Survival Analysis of Heart Failure Patients: A Case Study in Jimma University Medical Center, Jimma, Ethiopia

10.21203/rs.3.rs-339250/v1 ◽

2021 ◽

Author(s):

Tafese Ashine Tefera ◽

Geremew Muleta ◽

Kenenisa Tadesse

Keyword(s):

Heart Failure ◽

Survival Time ◽

Failure Time ◽

Medical Center ◽

Accelerated Failure Time Model ◽

Accelerated Failure Time ◽

Data Set ◽

Heart Failure Patients ◽

Accelerated Failure ◽

Jimma University

Abstract Heart failure is failure of the heart to pump blood with normal efficiency and globally growing public health issue with high death rate over the world including Ethiopia. The aim of this study was to identify factors affecting the survival time of heart failure patients in Jimma University Medical Center. To reach the aim, 409 heart failure patients were including in the study based on data taken from medical record of patients enrolled during January, 2016 to January, 2019. Kaplan Meier plots and log rank test were used for comparison of survival function; Bayesian survival models was used to identify factors affecting the survival time heart failure patients. Of the total patients in the study 164 (40.1%) were died. The estimated median survival time of patients was 31 months. Bayesian log-normal accelerated failure time model fit heart failure data-set better than other Bayesian accelerated failure time models used in this study. From the results of this model shows that the survival time of heart failure patients significantly affected by age, chronic kidney disease, diabetes mellitus, etiology of heart failure, hypertension, anemia, smoking cigarette and stages of heart failure. Bayesian log-normal accelerated failure time model describes the heart failure data-set well. Age group (49 to 65 years and greater than 65 years); etiology of heart failure (rheumatic valvular heart disease, hypertensive heart disease and Other diseases); presence of hypertension; presence of anemic; presence of chronic kidney disease; smokers; diabetes mellitus (type I and type II diabetic); and stages of heart failure (II, III and IV) were prolong the timing death of heart failure patients. The hospital, Jimma University medical center, need to improve public awareness for early detection of heart failure.

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Accelerated Failure Time Shared Frailty Models: Application to HIV/AIDS Patients on Anti-Retroviral Therapy in Delhi, India

Turkiye Klinikleri Journal of Biostatistics ◽

10.5336/biostatic.2015-48400 ◽

2016 ◽

Vol 8 (1) ◽

pp. 13-20 ◽

Cited By ~ 1

Author(s):

Prafulla Kumar SWAIN ◽

Gurprit GROVER

Keyword(s):

Failure Time ◽

Frailty Models ◽

Accelerated Failure Time ◽

Aids Patients ◽

Shared Frailty ◽

Accelerated Failure ◽

Hiv Aids

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Accelerated failure time modelling of Tuberculosis predictors in HIV/AIDS patients in Albert Luthuli Municipality of South Africa

10.21203/rs.3.rs-71793/v1 ◽

2020 ◽

Author(s):

Pepukai Bengura ◽

Principal Ndlovu ◽

Mulalo Annah Managa

Keyword(s):

Public Health ◽

South Africa ◽

Failure Time ◽

Accelerated Failure Time ◽

Aids Patients ◽

Factors Associated ◽

Aft Model ◽

Accelerated Failure ◽

Hiv Aids

Abstract Background: Tuberculosis (TB) is one of the most common opportunistic diseases and leading cause of death among Human immunodeficiency virus and acquired immune deficiency syndrome (HIV/AIDS) patients. There has been a drastic rise of TB infection associated with the pandemic occurrence of HIV/AIDS infection in South Africa and other resource-limited countries world-wide. South Africa faces an immense burden on health care systems posed by diagnostic and therapeutic challenges resulting from the concomitant HIV and TB epidemics. This study aimed to determine the prevalence and the factors associated with TB and HIV co-infection for patients attending clinical care at rural public health facilities in Albert Luthuli municipality of South Africa. Methods: A cohort of HIV/AIDS patients was retrospectively followed from inception in 2010 to 2017 until TB was diagnosed or until the end of the study. Accelerated Failure Time (AFT) model was used to analyse survival data on HIV/AIDS patients. Factors associated to TB were modelled using log-logistic AFT model and further analysis of the significant factors was done using Kaplan-Meier, log-rank and hazard ratios. Results: From 357 HIV/AIDS patients, 65 patients (18.2%) had TB. Out of the 65 HIV/AIDS patients with TB, 15 (23.1%) of them died. Thus, of the 41 HIV/AIDS patients who died during the follow-up period, 15 of them (36.6%) had TB. Log-logistic AFT model determined factors associated with TB at significance level of 0.05 as: hospital, WHO stage, treatment (regimen 1), ART adherence, follow-up CD4 count, baseline haemoglobin, follow-up white blood cell count, baseline viral load, baseline sodium and follow-up alanine transaminase. Discussion: Although antiretroviral therapy is effective in reducing the risk of developing TB, the overall burden of TB in HIV/AIDS community may not substantially diminish.Conclusion: TB/HIV co-infection is one of the serious public health problems in Albert Luthuli municipality. Collaborative TB/HIV activities in form of early diagnosis of both TB and HIV need a holistic approach in order to reduce drug resistance, drug toxicity, co-morbidities and mortalities which are associated with TB/HIV co-infection.

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Assessing survival time of heart failure patients: using Bayesian approach

Journal Of Big Data ◽

10.1186/s40537-021-00537-4 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Tafese Ashine ◽

Geremew Muleta ◽

Kenenisa Tadesse

Keyword(s):

Heart Failure ◽

Survival Time ◽

Failure Time ◽

Accelerated Failure Time Model ◽

Accelerated Failure Time ◽

Time Model ◽

Heart Failure Patients ◽

Accelerated Failure ◽

Log Normal ◽

Failure Time Model

AbstractHeart failure is a failure of the heart to pump blood with normal efficiency and a globally growing public health issue with a high death rate all over the world, including Ethiopia. The goal of this study was to identify factors affecting the survival time of heart failure patients. To achieve the aim, 409 heart failure patients were included in the study based on data taken from medical records of patients enrolled from January 2016 to January 2019 at Jimma University Medical Center, Jimma, Ethiopia. The Kaplan Meier plots and log-rank test were used for comparison of survival functions; the Cox-PH model and the Bayesian parametric survival models were used to analyze the survival time of heart failure patients using R-software. Integrated nested Laplace approximation methods have been applied. Out of the total heart failure patients in the study, 40.1% died, and 59.9% were censored. The estimated median survival time of patients was 31 months. Using model selection criteria, the Bayesian log-normal accelerated failure time model was found to be appropriate. The results of this model show that age, chronic kidney disease, diabetes mellitus, etiology of heart failure, hypertension, anemia, smoking cigarettes, and stages of heart failure all have a significant impact on the survival time of heart failure patients. The Bayesian log-normal accelerated failure time model described the survival time of heart failure patient's data-set well. The findings of this study suggested that the age group (49 to 65 years, and greater than or equal to 65 years); etiology of heart failure (rheumatic valvular heart disease, hypertensive heart disease, and other diseases); the presence of hypertension; the presence of anemia; the presence of chronic kidney disease; smokers; diabetes mellitus (type I, and type II); and stages of heart failure (II, III, and IV) shortened their survival time of heart failure patients.

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COMPARISON OF STRATIFIED WEIBULL MODEL AND WEIBULL ACCELERATED FAILURE TIME (AFT) MODEL IN THE ANALYSIS OF CERVICAL CANCER SURVIVAL

Jurnal Teknologi ◽

10.11113/jt.v78.8970 ◽

2016 ◽

Vol 78 (6-4) ◽

Author(s):

Nurliyana Juhan ◽

Nuradhiathy Abd Razak ◽

Yong Zulina Zubairi ◽

Nyi Nyi Naing ◽

Che Haziqah Che Hussin ◽

...

Keyword(s):

Cervical Cancer ◽

Survival Time ◽

Median Survival ◽

Distant Metastasis ◽

Median Survival Time ◽

Failure Time ◽

Weibull Model ◽

Accelerated Failure Time ◽

Aft Model ◽

Accelerated Failure

Cervical cancer is the fourth most common cancer affecting women worldwide, after breast, colorectal, and lung cancers with 528 000 new cases every year. It is also the fourth most common cause of cancer death with 266 000 deaths in 2012 among women worldwide. In Malaysia it remains to be a great concern among clinicians; yet published works on survival of cervical cancer patients are somewhat limited. In this study, two survival regression models which are parametric Stratified Weibull model and Weibull Accelerated Failure Time (AFT) model are considered as the alternative and improvement of the well-known Cox proportional hazard model to evaluate the prognostic factor that effect on survival of patients with cervical cancer. Comparisons were made to find the best model. Data were taken from Hospital University Science Malaysia (HUSM) over a period of 12 years. From the analyses it was found that the AFT model was the most appropriate. The AFT model has shown that the median survival time for patient at stage III & IV (14 months) is about one third that of those at stages I & II (40 months) for the same distant metastasis group. While, the median survival time for patient with distant metastasis (17 months) is half that of those without distant metastasis (34 months) for the same stage group.

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Bayesian Joint Modelling of Survival of HIV/AIDS Patients Using Accelerated Failure Time Data and Longitudinal CD4 Cell Counts

British Journal of Medicine and Medical Research ◽

10.9734/bjmmr/2017/32123 ◽

2017 ◽

Vol 20 (6) ◽

pp. 1-12 ◽

Cited By ~ 1

Author(s):

Markos Erango ◽

Ayele Goshu ◽

Gemeda Buta ◽

Ahmed Dessiso

Keyword(s):

Failure Time ◽

Accelerated Failure Time ◽

Failure Time Data ◽

Time Data ◽

Aids Patients ◽

Joint Modelling ◽

Cell Counts ◽

Cd4 Cell Counts ◽

Accelerated Failure ◽

Cd4 Cell

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Indicators of Adherence to Antiretroviral Therapy Treatment Among HIV/AIDS Patients in 5 African Countries

Journal of the International Association of Physicians in AIDS Care ◽

10.1177/1545109710361383 ◽

2010 ◽

Vol 9 (2) ◽

pp. 98-103 ◽

Cited By ~ 36

Author(s):

Martine Etienne ◽

Mian Hossain ◽

Robert Redfield ◽

Kristen Stafford ◽

Anthony Amoroso

Keyword(s):

Antiretroviral Therapy ◽

African Countries ◽

Aids Patients ◽

Therapy Treatment ◽

Hiv Aids

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IMPLEMENTASI MODEL ACCELERATED FAILURE TIME (AFT) BERDISTRIBUSI LOG-LOGISTIK PADA PASIEN PENYAKIT JANTUNG BAWAAN

Jurnal Gaussian ◽

10.14710/j.gauss.v10i4.33099 ◽

2022 ◽

Vol 10 (4) ◽

pp. 518-531

Author(s):

Dwi Nooriqfina ◽

Sudarno Sudarno ◽

Rukun Santoso

Keyword(s):

Pulmonary Hypertension ◽

Survival Time ◽

Failure Time ◽

Defect Size ◽

Accelerated Failure Time ◽

Hypertension Status ◽

Aft Model ◽

Pain Status ◽

Accelerated Failure ◽

First Recurrence

Log-Logistic Accelerated Failure Time (AFT) model is survival analysis that is used when the survival time follows Log-Logistic distribution. Log-Logistic AFT model can be used to estimate survival time, survival function, and hazard function. Log-Logistic AFT model was formed by regressing covariates linierly against the log of survival time. Regression coefficients are estimated using maximum likelihood method. This study uses data from Atrial Septal Defect (ASD) patients, which is a congenital disease with a hole in the wall that separates the top of two chambers of the heart by using sensor type III. Survival time as the response variable, that is the time from patient was diagnosed with ASD until the first relapse and uses age, gender, treatment status (catheterization/surgery), defect size that is the size of the hole in the heart terrace, pulmonary hypertension status, and pain status as predictor variables. The result showed that variable gender, treatment status, defect size, pulmonary hypertension status, and pain status affect the first recurrence of ASD patients, so it is found that category of female, untreated patient, defect size ≥12mm, having pulmonary hypertension, having chest pain tend to have first recurrence sooner than the other category.

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