Conditional Kaplan–Meier Estimator with Functional Covariates for Time-to-Event Data

Due to the wide availability of functional data from multiple disciplines, studies analysis have become popular in recent literature. However, related development censored survival has been relatively sparse. In this work, we consider problem analyzing time-to-event presence predictors. We develop a conditional generalized Kaplan–Meier (KM) estimator that incorporates predictors using kernel weights and rigorously establishes its asymptotic properties. addition, propose select optimal bandwidth based on time-dependent Brier score. then carry out extensive numerical examine finite sample performance proposed KM selector. also illustrated practical usage our method by set Alzheimer’s Disease Neuroimaging Initiative data.