Statistical Analysis of a Competing Risks Model with Weibull Sub-Distributions

Sarhan, Ammar M.; Gohary, Awad I. El; Tolba, Ahlam H.

doi:10.4236/am.2017.811121

Cited by 14 publications

(15 citation statements)

References 17 publications

(18 reference statements)

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“…In this study, generalized Weibull was employed because of its shape parameter (as an extra parameter compared to classic Weibull) which leads to covering different types of hazard functions (15) and it is suggested to use other parametric distribution such as Log-logistic which could reflect the same flexibility as Weibull distribution to analyze the competing risk survival.…”

Section: Discussionmentioning

confidence: 99%

“…Because the survival curves were matched for patients who were with a BMI less than 18.5 and people with a BMI between 18.5 and 24.9; therefore, we have integrated the two categories. Prognostic variables were entered in a parametric model called generalized Weibull distribution (15), in order to analyze the survival and its prognosis, in the presence of competing risk. Also Weibull model and Fine and Gray model (as proportional hazards semi parametric model for competing risks) were employed to analyze the data.…”

Section: Methodsmentioning

confidence: 99%

“…Generalized Weibull distribution is a distribution with an extra parameter (compared to classic Weibull), which could be more flexible for analyzing the competing risk model due to its ability to cover different types of hazard functions (15).…”

Section: Introductionmentioning

confidence: 99%

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Demographic and Pathological Predictors of Colorectal Cancer Survival in Competing Risk Model, Using Generalized Weibull Distribution

et al. 2017

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Objectives: To investigate the prognosis factors of survival in patients with colorectal cancer in presence of competing risks, using generalized Weibull distribution, and to compare the results of Weibull model as well as semi parametric models. Methods:A total of 1462 patients with colorectal cancer, registered in cancer registry center of research institute of gastroenterology and liver disease (from 2004 to 2015), Taleghani hospital, Tehran, Iran entered this study. Death and the causes of death were confirmed via telephone contact to patients' families. The demographic and clinical features included age at diagnosis, sex, family history of CRC, body mass index (BMI), tumor size and tumor site extracted from hospital documents. Generalized Weibull distribution, Cox regression analysis and Fine-Gary model were used to assess the prognosis of CRC survival. The analysis was carried out using R software version 3.0.2. P value less than 0.05 were considered significant. Results:Overall, 1060 CRC patients with completed data were included in the analysis. 58% were men and the mean ± SD of age at diagnosis was 53.67 ± 0.46 years. The mean ± SD of survival time was 56.96 ± 1.46 with median = 45.5 months. 380 patients (35.5%) died from CRC and 49 patients (4.6%) died from other causes of death, such as myocardial infarction, stomach cancer, liver cancer etc. Generalized Weibull model with competing risk analysis and other models indicated age and BMI as the prognosis. Conclusions:This study indicated age and BMI as the prognosis, using a generalized Weibull model with competing risk analysis. Also according to the survival curve estimation, generalized Weibull model with competing risk has a better fit to the data, compared to Weibull model.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Demographic and Pathological Predictors of Colorectal Cancer Survival in Competing Risk Model, Using Generalized Weibull Distribution

et al. 2017

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“…In literature, many authors considered the independent and dependent cause of failure rates. For instance, Sarhan et al [3] discussed the competing risks model with the presence of covariates using Weibull subdistributions. In this regard and in most situations, the statistical analysis of competing risk sample data assumes independent and/or dependent causes of failure.…”

Section: Introductionmentioning

confidence: 99%

Estimation for Akshaya Failure Model with Competing Risks under Progressive Censoring Scheme with Analyzing of Thymic Lymphoma of Mice Application

Abushal

Kumar²,

Muse³

et al. 2022

Complexity

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In several experiments of survival analysis, the cause of death or failure of any subject may be characterized by more than one cause. Since the cause of failure may be dependent or independent, in this work, we discuss the competing risk lifetime model under progressive type-II censored where the removal follows a binomial distribution. We consider the Akshaya lifetime failure model under independent causes and the number of subjects removed at every failure time when the removal follows the binomial distribution with known parameters. The classical and Bayesian approaches are used to account for the point and interval estimation procedures for parameters and parametric functions. The Bayes estimate is obtained by using the Markov Chain Monte Carlo (MCMC) method under symmetric and asymmetric loss functions. We apply the Metropolis–Hasting algorithm to generate MCMC samples from the posterior density function. A simulated data set is applied to diagnose the performance of the two techniques applied here. The data represented the survival times of mice kept in a conventional germ-free environment, all of which were exposed to a fixed dose of radiation at the age of 5 to 6 weeks, which was used as a practice for the model discussed. There are 3 causes of death. In group 1, we considered thymic lymphoma to be the first cause and other causes to be the second. On the base of mice data, the survival mean time (cumulative incidence function) of mice of the second cause is higher than the first cause.

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“…In the case of generalized Weibull distribution, Sarhan et al [10] introduced the relative risk rates, 1  and 2  , due to cause 1 and 2, the relative risk due to cause 1 and cause 2 is defined as   …”

Section: International Journal Of Advanced Statistics and Probabilitymentioning

confidence: 99%

Maximum likelihood estimation based on type-i hybrid progressive censored competing risks data

Ashour

Azm²

2016

IJASP

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This paper is concerned with the estimators problems of the generalized Weibull distribution based on Type-I hybrid progressive censoring scheme (Type-I PHCS) in the presence of competing risks when the cause of failure of each item is known. Maximum likelihood estimates and the corresponding Fisher information matrix are obtained. We generalized Kundu and Joarder [7] results in the case of the exponential distribution while, the corresponding results in the case of the generalized exponential and Weibull distributions may be obtained as a special cases. A real data set is used to illustrate the theoretical results.

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Statistical Analysis of a Competing Risks Model with Weibull Sub-Distributions

Cited by 14 publications

References 17 publications

Demographic and Pathological Predictors of Colorectal Cancer Survival in Competing Risk Model, Using Generalized Weibull Distribution

Demographic and Pathological Predictors of Colorectal Cancer Survival in Competing Risk Model, Using Generalized Weibull Distribution

Estimation for Akshaya Failure Model with Competing Risks under Progressive Censoring Scheme with Analyzing of Thymic Lymphoma of Mice Application

Maximum likelihood estimation based on type-i hybrid progressive censored competing risks data

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