Application of alternative parametric models for the survival analysis of cancer patients
DOI:
https://doi.org/10.17843/rpmesp.2019.362.4269Keywords:
Survival Analysis, Probabilistic Models, Likelihood Functions, Kaplan-Meier Estimate, Colonic NeoplasmsAbstract
This article describes a methodology that allows an approach to alternative right-censored probabilistic models for the analysis of survival, different to those usually studied (exponential, gamma, Weibull, and log-normal distribution) since it is possible that the data do not always fit with sufficient precision due to existing distributions. The methodology used allows for greater flexibility when modeling extreme observations, generally located in the right tail of data distribution, which admits that some events still have the probability of occurring, which is not the case with traditional models and the Kaplan-Meier estimator, which estimates for the longest times, survival probabilities approximately equal to zero. To show the usefulness of the methodological proposal, we considered an application with real data that relates survival times of patients with colon cancer (CC).Downloads
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Published
2019-06-28
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Special Section
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Copyright (c) 2019 Revista Peruana de Medicina Experimental y Salud Pública
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
1.
Valencia-Orozco A, Parra-Lara LG, Martínez JW, Tovar-Cuevas JR. Application of alternative parametric models for the survival analysis of cancer patients. Rev Peru Med Exp Salud Publica [Internet]. 2019 Jun. 28 [cited 2024 Nov. 13];36(2):341-8. Available from: https://rpmesp.ins.gob.pe/index.php/rpmesp/article/view/4269