Semiparametric transformation model for competing risks data with cure fraction
Abstract
We propose a new method for the analysis of competing risks data with long term survivors. The proposed method enables us to estimate the overall survival probability and cure fraction simultaneously. We formulate the effect of covariates on cumulative incidence functions using linear transformation models. Estimating equations based on counting process are developed to estimate regression coefficients. The asymptotic properties of the estimators are studied using martingale theory. An extensive Monte Carlo simulation study is carried out to assess the finite sample performance of the proposed estimators. Finally, we illustrate our method using a real data set.
Cite
@article{arxiv.2007.02305,
title = {Semiparametric transformation model for competing risks data with cure fraction},
author = {Sudheesh K Kattumannil and Sreedevi E P and Sankaran P G},
journal= {arXiv preprint arXiv:2007.02305},
year = {2022}
}
Comments
We propose a new methodology in mixture cure rate model with competing risks data