Modeling excess hazard with time--to--cure as a parameter

TL;DR

This paper introduces a new excess hazard regression model that estimates the time-to-cure as a covariate-dependent parameter, improving the accuracy of cure time estimation in cancer survival analysis.

Contribution

The paper proposes a novel model incorporating time-to-cure as a parameter, extending cure models to better estimate delay to cure with maximum likelihood estimation.

Findings

Model performs well in simulation studies.

Application to cancer data demonstrates practical utility.

Offers a simple approach to estimate cure time more accurately.

Abstract

Cure models have been widely developed to estimate the cure fraction when some subjects never experience the event of interest. However these models were rarely focused on the estimation of the time-to-cure i.e. the delay elapsed between the diagnosis and "the time from which cure is reached", an important indicator, for instance to address the question of access to insurance or loans for subjects with personal history of cancer. We propose a new excess hazard regression model that includes the time-to-cure as a covariate dependent parameter to be estimated. The model is written similarly to a Beta probability distribution function and is shown to be a particular case of the non-mixture cure models. Parameters are estimated through a maximum likelihood approach and simulation studies demonstrate good performance of the model. Illustrative applications to two cancer data sets are provided and some limitations as well as possible extensions of the model are discussed. The proposed model offers a simple and comprehensive way to estimate more accurately the time-to-cure. Key words: Cancer; Cure model; Cure time; Net survival; Right to be forgotten.