Cause-specific hazard regression estimation for modified Weibull distribution under a class of non-informative priors

Rehman, Habbiburr; Chandra, Navin; Hosseini-Baharanchi, Fatemeh Sadat; Baghestani, Ahmad Reza; Pourhoseingholi, Mohamad Amin

doi:10.1080/02664763.2021.1882407

Cited by 9 publications

(5 citation statements)

References 31 publications

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“…For parametric regression modelling of survival time one could use some well known distribution for the baseline function. More details can be found in [18] and reference therein. The CSHF in terms of the well known Cox PH model turns out to be of the following form:

where

is the baseline CSHF and

is the

vector of regression coefficients of cause

.…”

Section: Parametric Cause Specific Quantile Functionsmentioning

confidence: 99%

Competing risks survival data under middle censoring—An application to COVID-19 pandemic

Rehman

Chandra

Jammalamadaka

2021

Healthcare Analytics

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Survival data is being analyzed here under the middle censoring scheme, using specifically quantile function modelling under competing risks. The use of middle censoring scheme has been shown to be very appropriate under the COVID-19 pandemic scenario. Cause-specific quantile inference under middle censoring is employed. Such quantile inferences are obtained through cumulative incidence function based on cause-specific proportional hazards model. The baseline lifetime is assumed to follow a very general parametric model namely the Weibull distribution, and is independent of the censoring mechanism. We obtain estimates of the unknown parameters and cause specific quantile functions under classical as well as a Bayesian set-up. A Monte Carlo simulation study assesses the relative performance of the different estimators. Finally, a real life data analysis is given applying the proposed methods.

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where

is the baseline CSHF and

is the

vector of regression coefficients of cause

.…”

Section: Parametric Cause Specific Quantile Functionsmentioning

confidence: 99%

Competing risks survival data under middle censoring—An application to COVID-19 pandemic

Rehman

Chandra

Jammalamadaka

2021

Healthcare Analytics

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“…Terefore, to capture the complex form of data, numerous updated and fexible modifcations of equation (1) have been studied. For example, Vanem and Fazeres-Ferradosa [4] studied the truncated translated Weibull model, Alotaibi et al [5] considered the alpha power Weibull distribution to deal with the engineering datasets, Tach [6] proposed a three-component additive Weibull distribution and applied it for modeling the reliability datasets, Abd El-Monsef et al [7] analyzed a reliability dataset using the Poisson modifed Weibull model, Dessalegn et al [8] introduced the modifed Weibull model for the bamboo fbrous dataset, Rehman et al [9] analyzed the failure data by using another modifed Weibull model, and Li et al [10] developed a three-parameter Weibull distribution for modeling the fracture datasets.…”

Section: Introductionmentioning

confidence: 99%

A New Sine-Based Probabilistic Approach: Theory and Monte Carlo Simulation with Reliability Application

Heydari,

Zare,

Shokri

et al. 2024

Journal of Mathematics

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Data modeling is a very crucial stage for decision making in applied sectors. Probability distributions are considered important tools for decision making. So far, numerous probability distributions have been developed and implemented. Most of these distributions are developed by introducing from one to eight additional parameters. Sometimes, the addition of new parameters leads to re-parameterization problems. To avoid such issues, we introduce a novel probabilistic approach. The proposed approach may be termed as a new weighted sine-G method. The beauty and key advantage of the new weighted sine-G method are that it has no additional parameters. Through using the new weighted sine-G method, a new weighted sine-Weibull distribution is introduced, which is a modification of the Weibull distribution. The estimators of the new model are also derived. Furthermore, a simulation study is carried out to evaluate the estimators of the new weighted sine-Weibull distribution. Finally, a practical application from the reliability sector is considered to evaluate the new weighted sine-Weibull distribution. Based on certain decision tools, it is observed that the proposed model is the best competing distribution for applying it in the reliability sector.

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“…Lee [18] provided the parametric quantile inference for CSH function with adjustment of covariates. Rehman et al [29] presented the survival analysis with competing risks under parametric PH model with Bayesian approach.…”

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

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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.

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Cause-specific hazard regression estimation for modified Weibull distribution under a class of non-informative priors

Cited by 9 publications

References 31 publications

Competing risks survival data under middle censoring—An application to COVID-19 pandemic

Competing risks survival data under middle censoring—An application to COVID-19 pandemic

A New Sine-Based Probabilistic Approach: Theory and Monte Carlo Simulation with Reliability Application

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

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