Variable Selection and Prediction for Longitudinal Data Using Bayesian Transfer Learning.

Music City Center 

The analysis of contemporary longitudinal data problems involve high-dimensional measurements of time-course data collected on a small number of observations. The estimation of such models with limited sample sizes in a target population poses substantial challenges and can lead to highly variable parameter estimates, unstable predictions, and lower power. In such instances, it is natural to borrow information from additional datasets with similar covariate-outcome relations to improve inference in the target data. Here, we develop a novel Bayesian transfer learning model for longitudinal data (BTLL). BTLL leverages mixture models for the discrepancies between pivotal parameters of the outcome models of the source and target studies to enhance the accuracy of the parameter estimates and enable data-adaptive information borrowing. BTLL aims to minimize the transfer of information from source studies that would introduce large bias into posterior inference in the target study. Extensive simulation studies show that BTLL improves the precision of parameter estimates in the target study substantially, and reduces the bias, in heterogeneous settings when the outcome.

Longitudinal data analysis

Bayesian mixture model

Transfer learning

Adaptive data borrowing

Mixed effect modeling 

Biopharmaceutical Section