Bayesian inference in a generalized log-logistic proportional hazards model for the analysis of competing risk data: An application to stem-cell transplanted patients data

Alaziz, Sundus Naji; Muse, Abdisalam Hassan; Jawad, Taghreed M.; Sayed-Ahmed, Neveen; Aldallal, Ramy; Yusuf, M.

doi:10.1016/j.aej.2022.06.051

Cited by 6 publications

(3 citation statements)

References 32 publications

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“…Gibbs sampling is a technique for generating a random distribution from the posterior distribution having to calculate its density [35]. Technique on Gibbs Sampling based on the arrangement Markov chain which converges in a posterior distribution [36]. Steps in the algorithm process Gibbs Sampling [37] :…”

Section: Methodsmentioning

confidence: 99%

Regression of Survival Dagum 3 Parameter Link Function in Hiv/Aids Patients in East Java

Mahmudah,

Ningrum

2024

BAREKENG: J. Math. & App.

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AIDS (Acquired Immuno Deficiency Syndrome) is is a symptom of blood cell disease which results in decreased immunity in humans. HIV/AIDS causes a decline in economic productivity, health, and environmental fields, so it needs treatment to reduce the spread of the disease in East Java. The spread of the HIV/AIDS virus is growing rapidly both in terms of region and pattern of spread. Therefore, it is necessary to have policies from the government to prevent the spread of HIV/AIDS through mathematical modeling to determine the actual and comprehensive factors that influence HIV/AIDS care. policies needed to handle and reduce HIV/AIDS in East Java. This study aims to determine the factors that influence the care of people with HIV/AIDS with Survival Regression modeling Dagum 3 Parameter link function. This model uses parameter estimation with a Bayesian analysis approach with the results that the factors that significantly influence the length of HIV/AIDS care in East Java are marital status (X5), absolute CD4 levels (X7), Suffering Stage (X8), Functional State (X9), and Therapy adherence (X10). These results indicate that marital status and condition characteristics of HIV/AIDS sufferers are important information in the socialization program for reducing HIV/AIDS in East Java.

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Section: Methodsmentioning

confidence: 99%

Regression of Survival Dagum 3 Parameter Link Function in Hiv/Aids Patients in East Java

Mahmudah,

Ningrum

2024

BAREKENG: J. Math. & App.

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“…In this section, we obtain the Bayesian estimators of R, MRS, FSS, and FMRS based on the Lindley distribution. Let θ 1 and θ 2 be two independent random variables with gamma prior distribution where θ 1 ≈ Gamma a 1 , b 1 and θ 2 ≈ Gamma a 2 , b 2 [19][20][21]. Then, the joint prior distribution of θ 1 and θ 2 is…”

Section: Bayesian Estimation Of Fss and Fmrsmentioning

confidence: 99%

Fuzzy Stress‐Strength Model and Mean Remaining Strength for Lindley Distribution: Estimation and Application in Cancer of Benign Endocrine

Hassan,

Muse

2023

Computational and Mathematical Methods in Medicine

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This paper is interested in the Bayesian and non-Bayesian estimation of the stress-strength model and the mean remaining strength when there is fuzziness for stress and strength random variables having Lindley’s distribution with different parameters. A fuzzy is defined as a function of the difference between stress and strength variables. In the context of Bayesian estimation, two approximate algorithms are used importance sampling algorithm and the Monte Carlo Markov chain algorithm. For non-Bayesian estimation, maximum likelihood estimation and maximum product of spacing method are used. The Monte Carlo simulation study is performed to compare between different estimators for our proposed models using statistical criteria. Finally, to show the ability of our proposed models in real life, real medical application is introduced.

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“…S.N. Al-Aziz et al introduced a Bayesian methodology for analyzing competing risk data, utilizing a generalized log-logistic baseline distribution for the proportional hazard (PH) specification [ 20 ]. Traditional statistical inference techniques typically rely on estimating parameters using available data, with the maximum likelihood estimator (MLE) often being the preferred method.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the survival time of patients with heart failure with reduced ejection fraction: a Bayesian approach via a competing risk parametric model

Norouzi,

Hajizadeh,

Jafarabadi

et al. 2024

BMC Cardiovasc Disord

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Purpose Heart failure (HF) is a widespread ailment and is a primary contributor to hospital admissions. The focus of this study was to identify factors affecting the extended-term survival of patients with HF, anticipate patient outcomes through cause-of-death analysis, and identify risk elements for preventive measures. Methods A total of 435 HF patients were enrolled from the medical records of the Rajaie Cardiovascular Medical and Research Center, covering data collected between March and August 2018. After a five-year follow-up (July 2023), patient outcomes were assessed based on the cause of death. The survival analysis was performed with the AFT method with the Bayesian approach in the presence of competing risks. Results Based on the results of the best model for HF-related mortality, age [time ratio = 0.98, confidence interval 95%: 0.96–0.99] and ADHF [TR = 0.11, 95% (CI): 0.01–0.44] were associated with a lower survival time. Chest pain in HF-related mortality [TR = 0.41, 95% (CI): 0.10–0.96] and in non-HF-related mortality [TR = 0.38, 95% (CI): 0.12–0.86] was associated with a lower survival time. The next significant variable in HF-related mortality was hyperlipidemia (yes): [TR = 0.34, 95% (CI): 0.13–0.64], and in non-HF-related mortality hyperlipidemia (yes): [TR = 0.60, 95% (CI): 0.37–0.90]. CAD [TR = 0.65, 95% (CI): 0.38–0.98], CKD [TR = 0.52, 95% (CI): 0.28–0.87], and AF [TR = 0.53, 95% (CI): 0.32–0.81] were other variables that were directly related to the reduction in survival time of patients with non-HF-related mortality. Conclusion The study identified distinct predictive factors for overall survival among patients with HF-related mortality or non-HF-related mortality. This differentiated approach based on the cause of death contributes to the estimation of patient survival time and provides valuable insights for clinical decision-making.

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Bayesian inference in a generalized log-logistic proportional hazards model for the analysis of competing risk data: An application to stem-cell transplanted patients data

Cited by 6 publications

References 32 publications

Regression of Survival Dagum 3 Parameter Link Function in Hiv/Aids Patients in East Java

Regression of Survival Dagum 3 Parameter Link Function in Hiv/Aids Patients in East Java

Fuzzy Stress‐Strength Model and Mean Remaining Strength for Lindley Distribution: Estimation and Application in Cancer of Benign Endocrine

Analysis of the survival time of patients with heart failure with reduced ejection fraction: a Bayesian approach via a competing risk parametric model

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