A Flexible Bayesian Parametric Proportional Hazard Model: Simulation and Applications to Right-Censored Healthcare Data

Muse, Abdisalam Hassan; Ngesa, Oscar; Mwalili, Samuel; Alshanbari, Huda M.; El-Bagoury, Abd Al-Aziz Hosni

doi:10.1155/2022/2051642

Cited by 9 publications

(5 citation statements)

References 37 publications

(57 reference statements)

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“…The three common regression models in the context of hazard-based regression models are: PH [2], AFT [18], and accelerated hazard (AH) [20] models. On the other hand, the three most popular regression models in the context of odds-based regression models are proportional odds (PO) [29], accelerated odds (AO) [28], and AFT models.…”

Section: Aft Model Formulationmentioning

confidence: 99%

The Extended Exponential-Weibull Accelerated Failure Time Model with Application to Sudan COVID-19 Data

Mastor¹,

Alghamdi

Ngesa

et al. 2023

Mathematics

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A fully parametric accelerated failure time (AFT) model with a flexible, novel modified exponential Weibull baseline distribution called the extended exponential Weibull accelerated failure time (ExEW-AFT) model is proposed. The model is presented using the multi-parameter survival regression model, where more than one distributional parameter is linked to the covariates. The model formulation, probabilistic functions, and some of its sub-models were derived. The parameters of the introduced model are estimated using the maximum likelihood approach. An extensive simulation study is used to assess the estimates’ performance using different scenarios based on the baseline hazard shape. The proposed model is applied to a real-life right-censored COVID-19 data set from Sudan to illustrate the practical applicability of the proposed AFT model.

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Section: Aft Model Formulationmentioning

confidence: 99%

The Extended Exponential-Weibull Accelerated Failure Time Model with Application to Sudan COVID-19 Data

Mastor¹,

Alghamdi

Ngesa

et al. 2023

Mathematics

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“…The difference between models is that the baseline hazard function ℎ 0 (𝑡) of the PH model is assumed to follow a specific distribution (Muse et al, 2022). In this case, the distribution used is the Burr XII distribution so that the baseline hazard function follows the Burr XII distribution.…”

Section: Survival Burr XII Regression Modelmentioning

confidence: 99%

Parametric Survival Model on IPB University’s Graduation Data

Nashiruddin

Sumarno

Budiarti

2023

JTAM

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Graduation is one of the assessment criteria in the college accreditation process. Students who graduate on time will assist in the assessment of college accreditation. This study aims to determine the distribution that best fits student graduation data and determine the best model to analyze the factors that determine student graduation from IPB University. This study presents some parametric models in survival analysis, specifically, the accelerated failure time (AFT) model and the proportional hazard (PH) model. The objective of this research is to compare the performance of PH model and the AFT models in analyzing the significant factors affecting the student graduation at the IPB University. Based on the study's results, the distribution according to student graduation data is the Burr XII distribution, and the best model using the AIC criteria is the PH Burr XII model. The factors that influence the graduation of IPB University students are gender, faculty, GPA, regional origin, and school status.

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“…Since the gamma G(•) density provides various shapes based on parameter values and is flexible in nature, so the utilizing of independent gamma priors are relatively simple which may yield to results with more explicit posterior density expressions Muse et al [19]; for more details, it can be seen in [14,20,21]. us, the gamma conjugate priors…”

Section: Prior Information and Loss Functionsmentioning

confidence: 99%

Estimation for Parameters of Life of the Marshall‐Olkin Generalized‐Exponential Distribution Using Progressive Type‐II Censored Data

et al. 2022

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A new three-parameter extension of the generalized-exponential distribution, which has various hazard rates that can be increasing, decreasing, bathtub, or inverted tub, known as the Marshall-Olkin generalized-exponential (MOGE) distribution has been considered. So, this article addresses the problem of estimating the unknown parameters and survival characteristics of the three-parameter MOGE lifetime distribution when the sample is obtained from progressive type-II censoring via maximum likelihood and Bayesian approaches. Making use of the s-normality of classical estimators, two types of approximate confidence intervals are constructed via the observed Fisher information matrix. Using gamma conjugate priors, the Bayes estimators against the squared-error and linear-exponential loss functions are derived. As expected, the Bayes estimates are not explicitly expressed, thus the Markov chain Monte Carlo techniques are implemented to approximate the Bayes point estimates and to construct the associated highest posterior density credible intervals. The performance of proposed estimators is evaluated via some numerical comparisons and some specific recommendations are also made. We further discuss the issue of determining the optimum progressive censoring plan among different competing censoring plans using three optimality criteria. Finally, two real-life datasets are analyzed to demonstrate how the proposed methods can be used in real-life scenarios.

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A Flexible Bayesian Parametric Proportional Hazard Model: Simulation and Applications to Right-Censored Healthcare Data

Cited by 9 publications

References 37 publications

The Extended Exponential-Weibull Accelerated Failure Time Model with Application to Sudan COVID-19 Data

The Extended Exponential-Weibull Accelerated Failure Time Model with Application to Sudan COVID-19 Data

Parametric Survival Model on IPB University’s Graduation Data

Estimation for Parameters of Life of the Marshall‐Olkin Generalized‐Exponential Distribution Using Progressive Type‐II Censored Data

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