Prediction and forecasting models based on patient's history and biomarkers with application to Scleroderma disease

This paper aims at predicting lung function values based on patients historical lung function values and serum biomarkers in Scleroderma patients. The progression of disease is measured by three lung function indexes (FVC, TLC, DLCO). Values of four biomarkers (TIMP1, P3NP, HA, NT-proBNP) are available. The data are sparse (6 months intervals) and irregular (many visits are missed). We consider two modeling approaches to achieve our goal, namely, the mixed effects model which is the standard approach in epidemiological studies and the functional principal component analysis model which is typically used for dense temporal datasets. We find that functional data methodology was able to recover the trajectories of three lung function indexes and to predict the future values very well.