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Abstract:
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In longitudinal studies, prognostic biomarkers are often measured longitudinally. It is of both scientific and clinical interest to predict the risk of clinical events using these biomarkers as well as other time-dependent and time-independent information about the patient. The prediction is dynamic in the sense that it can be made at any time during the follow-up, adapting to the changing at-risk population and incorporating the most recent longitudinal data. We present a general analytical framework using the landmark approach for dynamic prediction. The proposed framework allows the measurement times of longitudinal data to be irregularly spaced and differ between subjects. We propose a unified kernel weighting approach for estimating the model parameters, calculating predicted probabilities, and evaluating prediction accuracy through double time-dependent Receiver Operating Characteristics (ROC) curves. We illustrate the proposed analytical framework using the African American Study of Kidney Disease and Hypertension (AASK) to develop a landmark model for dynamic prediction of end-stage renal diseases or death among patients with chronic kidney disease.
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