Estimation of the Modified Weibull Additive Hazards Regression Model under Competing Risks

Rehman, Habbiburr; Chandra, Navin; Emura, Takeshi; Pandey, Manju

doi:10.3390/sym15020485

Cited by 3 publications

(6 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There is no definitive rule for selecting the best prior distribution to formulate the Bayes estimator. However, in cases where only limited or vague knowledge about the parameters is available, a noninformative prior can be employed [ 21 ]. In this study, we utilized sensitivity analysis for the optimal selection and tuning of the prior distribution variance.…”

Section: Methodsmentioning

confidence: 99%

“…In this study, we utilized a normal distribution with a large variance (mean 0 and variance of 10,000; Non-Informative) as the prior distribution for the regression coefficients [ 21 ].…”

Section: Methodsmentioning

confidence: 99%

“…In contrast, Bayesian reasoning is renowned for its ability to incorporate prior information. Additionally, Bayesian methods provide more accurate estimation results than MLE [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

View full text Add to dashboard Cite

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.

show abstract

Section: Methodsmentioning

confidence: 99%

“…In this study, we utilized a normal distribution with a large variance (mean 0 and variance of 10,000; Non-Informative) as the prior distribution for the regression coefficients [ 21 ].…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

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

View full text Add to dashboard Cite

show abstract

“…The aim of this attempt is to consider both classical and Bayesian methods of estimation. Recently, Rehman et al [28] proposed the Bayesian estimation based on the class of informative priors for the modified Weibull AH regression model under competing risks. Therefore, in this article, we consider Bayesian estimation based on a class of non-informative types of prior namely, uniform, Jeffreys and half-t for baseline parameters and uniform prior for regression parameters.…”

Section: Introductionmentioning

confidence: 99%

Analysis and modelling of competing risks survival data using modified Weibull additive hazards regression approach

Rehman

Chandra

Abuzaid

2023

Hacettepe Journal of Mathematics and Statistics

Self Cite

View full text Add to dashboard Cite

The cause-specific hazard function plays an important role in developing the regression models for competing risks survival data. Proportional hazards and additive hazards are the commonly used regression approaches in survival analysis. Mostly, in literature, the proportional hazards model was used for parametric regression modelling of survival data. In this article, we introduce a parametric additive hazards regression model for survival analysis with competing risks. For employing a parametric model we consider the modified Weibull distribution as a baseline model which is capable to model survival data with non-monotonic behaviour of hazard rate. The estimation process is carried out via maximum likelihood and Bayesian approaches. In addition to Bayesian methods, a class of non-informative types of prior is introduced with squared error (symmetric) and linear-exponential (asymmetric) loss functions. The relative performance of the different estimators is assessed using Monte Carlo simulation. Finally, using the proposed methodology, a real data analysis is performed.

show abstract

“…Rehman et al [11] deal with parametric regression analysis of survival data using the additive hazards model with competing risks in the presence of independent right censoring. The baseline hazard function is parameterized using a modified Weibull distribution as a lifetime model.…”

mentioning

confidence: 99%

Symmetrical and Asymmetrical Distributions in Statistics and Data Science

Johannssen,

Chukhrova,

Zhu

2023

Symmetry

View full text Add to dashboard Cite

show abstract

Estimation of the Modified Weibull Additive Hazards Regression Model under Competing Risks

Cited by 3 publications

References 41 publications

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

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

Analysis and modelling of competing risks survival data using modified Weibull additive hazards regression approach

Symmetrical and Asymmetrical Distributions in Statistics and Data Science

Contact Info

Product

Resources

About