ArticleJournal of applied statistics2022
A proportional-hazards model for survival analysis and long-term survivors modeling: application to amyotrophic lateral sclerosis data.
Article in Journal of applied statistics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- An Interpretable Deep Learning Framework for Biomarker Discovery in Complex Disease Survival Outcomes.bioRxiv : the preprint server for biology · 2025Article
- Cuproptosis-related risk score based on machine learning algorithm predicts prognosis and characterizes tumor microenvironment in head and neck squamous carcinomas.Scientific reports · 2023Article
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2 authors.
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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.
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