  Increasingly during the past decade, researchers have sought to leverage
auxiliary data for enhancing individualized inference. Many existing methods,
such as multisource exchangeability models (MEM), have been developed to borrow
information from multiple supplemental sources to support parameter inference
in a primary source. MEM and its alternatives decide how much information to
borrow based on the exchangeability of the primary and supplemental sources,
where exchangeability is defined as equality of the target parameter. Other
information that may also help determine the exchangeability of sources is
ignored. In this article, we propose a generalized Reinforced Borrowing
Framework (RBF) leveraging auxiliary data for enhancing individualized
inference using a distance-embedded prior which utilizes data not only about
the target parameter, but also uses different types of auxiliary information
sources to "reinforce" inference on the target parameter. RBF improves
inference with minimal additional computational burden. We demonstrate the
application of RBF to a study investigating the impact of the COVID-19 pandemic
on individual activity and transportation behaviors, where RBF achieves 20-40%
lower MSE compared with existing methods.
