Landmark linear transformation model for dynamic prediction with application to a longitudinal cohort study of chronic disease

Dynamic prediction of the risk of a clinical event by using longitudinally measured biomarkers or other prognostic information is important in clinical practice. We propose a new class of landmark survival models. The model takes the form of a linear transformation model but allows all the model parameters to vary with the landmark time. This model includes many published landmark prediction models as special cases. We propose a unified local linear estimation framework to estimate time varying model parameters. Simulation studies are conducted to evaluate the finite sample performance of the method proposed. We apply the methodology to a data set from the African American Study of Kidney Disease and Hypertension and predict individual patients’ risk of an adverse clinical event.