A Bayesian transfer learning model for mixed-effects longitudinal data analysis.

The analysis of contemporary longitudinal data problems often involves high-dimensional measurements of time-course data collected on a small number of observations. The estimation of such models with limited sample sizes can pose substantial challenges and lead to highly variable estimates, unstable predictions, and low power. In such instances, it is natural to borrow information from additional datasets with similar, although not necessarily identical, covariate-outcome relations to improve inference in the target population. We develop a novel Bayesian transfer learning model for longitudinal data (BTLL). BTLL leverages mixture models for the discrepancies between the pivotal parameters of the outcome models of the target and source studies to enhance estimation accuracy and enable data-adaptive information borrowing. BTLL aims to minimize the negative transfer of information from source studies that would otherwise introduce large bias into inference in the target population. Through extensive simulations and real data applications, we show that BTLL improves the precision of parameter estimates in the target study substantially compared to alternative methods. Moreover, our BTLL reduces the estimation bias in heterogeneous settings when the outcome distributions of the target and source datasets deviate from each other. Motivated by the progressive nature of dementia and the importance of early detection, we develop a BTLL prediction model using datasets for individuals with mild cognitive impairment. We show that BTLL can leverage non-invasive biomarkers to identify subjects at high risk for dementia and consistently achieves the lowest prediction error.