Dynamic prediction of disease progression for leukemia patients by functional principal component analysis of longitudinal expression levels of an oncogene

Abstract

Patients’ biomarker data are repeatedly measured over time during their follow-up visits. Statistical models are needed to predict disease progression on the basis of these longitudinal biomarker data. Such predictions must be conducted on a real-time basis so that at any time a new biomarker measurement is obtained, the prediction can be updated immediately to reflect the patient’s latest prognosis and further treatment can be initiated as necessary. This is called dynamic prediction. The challenge is that longitudinal biomarker values fluctuate over time, and their changing patterns vary greatly across patients. In this article, we apply functional principal components analysis (FPCA) to longitudinal biomarker data to extract their features, and use these features as covariates in a Cox proportional hazards model to conduct dynamic predictions. Our flexible approach comprehensively characterizes the trajectory patterns of the longitudinal biomarker data. Simulation studies demonstrate its robust performance for dynamic prediction under various scenarios. The proposed method is applied to dynamically predict the risk of disease progression for patients with chronic myeloid leukemia following their treatments with tyrosine kinase inhibitors. The FPCA method is applied to their longitudinal measurements of BCR-ABL gene expression levels during follow-up visits to obtain the changing patterns over time as predictors.