A proportional-hazards model for survival analysis and long-term survivors modeling: application to amyotrophic lateral sclerosis data

Tasnime Hamdeni and Soufiane Gasmi

Journal of Applied Statistics, 2022, vol. 49, issue 3, 694-708

Abstract: 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.

Date: 2022
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DOI: 10.1080/02664763.2020.1830954

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