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

TL;DR

This paper introduces two new regression models, defective Gompertz and defective inverse Gaussian, for analyzing interval-censored competing risk data with cured individuals, allowing direct estimation of cure fractions and cause-specific failure risks.

Contribution

The paper develops novel defective regression models for interval-censored competing risks data that incorporate cure fractions and provides maximum likelihood estimation methods.

Findings

Models accurately estimate cure fractions and cause-specific risks.

Simulation studies demonstrate good finite sample performance.

Application to HIV data shows practical utility.

Abstract

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.