Predicting patient life expectancy is of great importance for clinicians in making treatment decisions. This prediction needs to be conducted in a dynamic manner, based on longitudinal biomarkers repeatedly measured during the patient's post-treatment follow-up period. The prediction is updated any time a new biomarker measurement is obtained. The heterogeneity across patients of biomarker trajectories over time requires flexible and powerful approaches to model noisy and irregularly measured longitudinal data. In this article, we use functional principal component analysis (FPCA) to extract the dominant features of the biomarker trajectory of each individual, and use these features as time-dependent predictors (covariates) in a transformed mean residual life (MRL) regression model to conduct dynamic prediction. Simulation studies demonstrate the improved performance of the transformed MRL model that includes longitudinal biomarker information in the prediction. We apply the proposed method to predict the remaining time expectancy until disease progression for patients with chronic myeloid leukemia, using the transcript levels of an oncogene, BCR-ABL.
Keywords: Life expectancy; Longitudinal data; Stochastic process; Supermodel; Survival analysis.
© 2018, The International Biometric Society.