A proportional-hazards model for survival analysis and long-term survivors modeling : application to amyotrophic lateral sclerosis data
© 2020 Informa UK Limited, trading as Taylor & Francis Group.
| Veröffentlicht in: | Journal of applied statistics. - 1991. - 49(2022), 3 vom: 01., Seite 694-708 |
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| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2022
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| Zugriff auf das übergeordnete Werk: | Journal of applied statistics |
| Schlagworte: | Journal Article 62Exx 62Fxx 62Jxx 62Nxx 62P10 Amyotrophic lateral sclerosis defective modeling parameter estimation proportional-hazards |
| Zusammenfassung: | © 2020 Informa UK Limited, trading as Taylor & Francis Group. 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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| Beschreibung: | Date Revised 26.08.2024 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
| ISSN: | 0266-4763 |
| DOI: | 10.1080/02664763.2020.1830954 |