Regression Modeling of Competing Risks Survival Data in the Presence of Covariates Based on a Generalized Weibull Distribution: A Simulation Study

Soraya Moamer, Ahmad Reza Baghestani, Mohamad Amin Pourhoseingholi

Abstract


In survival analysis or medical studies each person can be exposed to more than one type of outcomes which occurrence of one of them prevents the other outcomes' occurrence; this situation is called the competing risks. Assessing the effect of covariates on the survival time (or failure time) is one of the purposes in competing risks analysis. In this paper, we study a competing risks model in the presence of covariates when the causes of failures follow generalized Weibull distributions. Covariates are entered to the model through the scale parameter of this distribution. Also in this study the competing risks are considered to be independent. Parameter estimation has been done by the maximum likelihood approach, in a real data set and a simulation study has shown the advantages of proposed model.


Keywords


Competing risks models, Generalized Weibull Distribution, Covariates, Survival analysis

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DOI: http://dx.doi.org/10.18187/pjsor.v14i2.1929

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Title

Regression Modeling of Competing Risks Survival Data in the Presence of Covariates Based on a Generalized Weibull Distribution: A Simulation Study

Keywords

Competing risks models, Generalized Weibull Distribution, Covariates, Survival analysis

Description

In survival analysis or medical studies each person can be exposed to more than one type of outcomes which occurrence of one of them prevents the other outcomes' occurrence; this situation is called the competing risks. Assessing the effect of covariates on the survival time (or failure time) is one of the purposes in competing risks analysis. In this paper, we study a competing risks model in the presence of covariates when the causes of failures follow generalized Weibull distributions. Covariates are entered to the model through the scale parameter of this distribution. Also in this study the competing risks are considered to be independent. Parameter estimation has been done by the maximum likelihood approach, in a real data set and a simulation study has shown the advantages of proposed model.


Date

2018-06-01

Identifier


Source

Pakistan Journal of Statistics and Operation Research; Vol. 14 No. 2, 2018



Print ISSN: 1816-2711 | Electronic ISSN: 2220-5810