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Abstract:
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New statistical methods are proposed for conducting predictions on a real-time basis so that at any time during follow-up, as soon as a new biomarker value is obtained, the predictions can be updated immediately to reflect the latest prognosis. These methods include quantile regression on residual survival time, functional principal component analysis for summarizing the changing patterns of patients' longitudinal biomarker trajectories, and a supermodel to smoothly extend landmark analyses on discrete time points to the whole follow-up time interval. Simulation studies show that the proposed approaches achieve stable estimation of biomarker effects over time, and are robust against model misspecification. Moreover, they have better predictive performance than current methods, as evaluated by the root mean square error and area under the curve of receiver's operating characteristics. The proposed methods are applied to a data set of patients with chronic myeloid leukemia. At any time following their treatment with tyrosine kinase inhibitors, longitudinally measured transcript levels of the oncogene BCR-ABL are used to predict patients' risks of disease progression.
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