Semiparametric transformation model for competing risks data with cure fraction

TL;DR

This paper introduces a semiparametric transformation model for competing risks data with cure fraction, allowing simultaneous estimation of survival probability and cure rate, supported by theoretical properties and simulation studies.

Contribution

It develops a novel semiparametric model with counting process-based estimating equations for competing risks data with cure fraction, including asymptotic analysis and practical application.

Findings

Estimators have good finite sample performance in simulations.

The method accurately estimates survival and cure fractions.

Application to real data demonstrates practical utility.

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.