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

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  • Tasnime Hamdeni
  • Soufiane Gasmi

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.

Suggested Citation

  • Tasnime Hamdeni & Soufiane Gasmi, 2022. "A proportional-hazards model for survival analysis and long-term survivors modeling: application to amyotrophic lateral sclerosis data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 49(3), pages 694-708, February.
    • Handle: RePEc:taf:japsta:v:49:y:2022:i:3:p:694-708
    DOI: 10.1080/02664763.2020.1830954
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