Inference at the individual subject level.

In this work, I developed methodologies for prediction of growth trajectories for individual subjects based on past measurements and population statistics. Prediction is described in the context of nonlinear mixed effects modeling (NLME) where the full reference population’s statistics (estimated fixed effects, variance-covariance of random effects, variance of noise) is used along with the individual’s available observations to predict its trajectory. The proposed methodology is generic in regard to application domains and results in prediction at the individual level with potential for use in personalized medicine.

Papers/abstracts: Sadeghi et al., MICCAI 2014