A new latent cure rate marker model for survival data

TL;DR

This paper introduces a novel mixture model using latent cure rate markers for survival data with a cure fraction, improving risk classification and predictive accuracy in clinical settings.

Contribution

The paper proposes a new latent cure rate marker model that classifies patients into risk groups and enhances data fitting over existing cure rate models.

Findings

Better fit to prostate cancer trial data

Effective risk group classification

Supports determining number of risk groups

Abstract

To address an important risk classification issue that arises in clinical practice, we propose a new mixture model via latent cure rate markers for survival data with a cure fraction. In the proposed model, the latent cure rate markers are modeled via a multinomial logistic regression and patients who share the same cure rate are classified into the same risk group. Compared to available cure rate models, the proposed model fits better to data from a prostate cancer clinical trial. In addition, the proposed model can be used to determine the number of risk groups and to develop a predictive classification algorithm.