Dynamic Long-Term Prediction With Intermediate Event Information: A Flexible Model With Bivariate Time-Varying Coefficients.

ABSTRACT: The integration of time-to-intermediate event data and the evolving characteristics of patients to enhance long-term prediction has garnered significant interest, driven by the wealth of data generated from longitudinal cohorts. In this paper, we propose sequential/dynamic prediction rules by using regression models with time-varying coefficients. We introduce a class of dynamic models that not only incorporates intermediate event information but also leverages information across different landmark times. To address the challenge of right-censoring, we employ an inverse weighting technique in the estimation process. We establish the asymptotic properties of the estimated parameters and conduct extensive simulations to assess the finite sample performance. Our simulation studies confirm tha

SUBMITTER: Wang Y 

PROVIDER: S-EPMC12374258 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature