A unified framework for complex survival data: accounting for clustering, cure fractions, and competing risks

Mixture cure models are widely used in survival analysis to represent populations comprising both cured and susceptible subgroups. Moving beyond conventional approaches that address isolated challenges, this study introduces an integrated systems framework for analyzing complex survival data. The proposed model simultaneously accounts for three interdependent components: (1) the existence of a cured fraction, (2) the presence of incurable competing risks, and (3) within-cluster correlations. To address these issues, we propose a novel clustered survival model that simultaneously accommodates both cure fractions and competing risks. Our approach incorporates a working correlation matrix within estimating equations to model dependence structures - both in cure probabilities among cluster members and in survival times of susceptible individuals. The estimation procedure utilizes an ES algorithm framework for efficient computation of regression parameters. We conduct comprehensive simulation studies to evaluate the model’s finite-sample performance, demonstrating satisfactory results in parameter estimation and inference. Finally, the practical utility of our methodology is illustrated through an application to clinical trial data, providing empirical evidence of its effectiveness in real-world scenarios.