A semiparametric accelerated failure time-based mixture cure tree.

ABSTRACT: The mixture cure rate model (MCM) is the most widely used model for the analysis of survival data with a cured subgroup. In this context, the most common strategy to model the cure probability is to assume a generalized linear model with a known link function, such as the logit link function. However, the logit model can only capture simple effects of covariates on the cure probability. In this article, we propose a new MCM where the cure probability is modeled using a decision tree-based classifier and the survival distribution of the uncured is modeled using an accelerated failure time structure. To estimate the model parameters, we develop an expectation maximization algorithm. Our simulation study shows that the proposed model performs better in capturing nonlinear classification bound

SUBMITTER: Aselisewine W 

PROVIDER: S-EPMC12035937 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature