A functional landmark flexible-hazards cure model for individual dynamic prediction

Abstract

Important biomarkers are routinely measured during patients’ follow-up visits to monitor their response to treatment and track disease progression. Accurate, individualized dynamic prediction based on data available at these visits is essential for making informed decisions regarding the next stage of treatment. We propose a versatile landmark model designed to address the complexities of real-world clinical scenarios, including (1) complex longitudinal biomarker trajectories not following a prespecified parametric function over time, (2) the potential for a fraction of patients to be cured, and (3) the violation of the proportional hazards assumption in the relationship between predictors and cure or survival outcomes. The proposed landmark model leverages functional principal component analysis (FPCA) to extract predictive features from longitudinal biomarker data, using the resulting functional principal component scores as predictors. Simulation studies demonstrate that the proposed model outperforms commonly used prediction models in terms of AUC values and Brier scores across a range of complex scenarios. We illustrate the practical utility of our model through its application to a study of chronic myeloid leukemia, showcasing its effectiveness in dynamically predicting individual conditional cure and survival probabilities.