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|a 10.1080/02664763.2020.1830954
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|a eng
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|a Hamdeni, Tasnime
|e verfasserin
|4 aut
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|a A proportional-hazards model for survival analysis and long-term survivors modeling
|b application to amyotrophic lateral sclerosis data
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|c 2022
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 26.08.2024
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|a published: Electronic-eCollection
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|a Citation Status PubMed-not-MEDLINE
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|a © 2020 Informa UK Limited, trading as Taylor & Francis Group.
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|a The majority of survival data are affected by explanatory variables. We develop a new regression model for survival data analysis. As an alternative to standard mixture models, another model is proposed to describe the eventual presence of a surviving fraction. The proposed models are based on the Marshall-Olkin extended generalized Gompertz distribution. A maximum-likelihood inference is presented in the presence of covariates and a censorship phenomenon. Explanatory variables are incorporated into the model through proportional-hazards to evaluate the effect of risk factors on overall survival under different assumptions. Parametric, semi-parametric, and non-parametric methods are applied to survival analysis of patients treated for amyotrophic lateral sclerosis. Interesting results about riluzole use and other treatment effects on patients' survival have been obtained
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|a Amyotrophic lateral sclerosis
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|a defective modeling
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|a parameter estimation
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| 650 |
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|a proportional-hazards
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|a Gasmi, Soufiane
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|t Journal of applied statistics
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