Regression Analysis of Cure Rate Models with Competing Risks Subjected to Interval Censoring

Fuente: arXiv
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Main Authors: K., Silpa, P., Sreedevi E., Sankaran, P. G.
Format: Preprint
Published: 2024
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author K., Silpa
P., Sreedevi E.
Sankaran, P. G.
author_facet K., Silpa
P., Sreedevi E.
Sankaran, P. G.
contents In this work, we present two defective regression models for the analysis of interval-censored competing risk data in the presence of cured individuals, viz., defective Gompertz and defective inverse Gaussian regression models. The proposed models enable us to estimate the cure fraction directly from the model. Simultaneously, we estimate the regression parameters corresponding to each cause of failure using the method of maximum likelihood. The finite sample behaviour of the proposed models is evaluated through Monte Carlo simulation studies. We illustrate the practical applicability of the models using a real-life data set on HIV patients.
format Preprint
id arxiv_https___arxiv_org_abs_2412_04803
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Regression Analysis of Cure Rate Models with Competing Risks Subjected to Interval Censoring
K., Silpa
P., Sreedevi E.
Sankaran, P. G.
Methodology
In this work, we present two defective regression models for the analysis of interval-censored competing risk data in the presence of cured individuals, viz., defective Gompertz and defective inverse Gaussian regression models. The proposed models enable us to estimate the cure fraction directly from the model. Simultaneously, we estimate the regression parameters corresponding to each cause of failure using the method of maximum likelihood. The finite sample behaviour of the proposed models is evaluated through Monte Carlo simulation studies. We illustrate the practical applicability of the models using a real-life data set on HIV patients.
title Regression Analysis of Cure Rate Models with Competing Risks Subjected to Interval Censoring
topic Methodology
url https://arxiv.org/abs/2412.04803