  Information integration plays a pivotal role in biomedical studies by
facilitating the combination and analysis of independent datasets from multiple
studies, thereby uncovering valuable insights that might otherwise remain
obscured due to the limited sample size in individual studies. However, sharing
raw data from independent studies presents significant challenges, primarily
due to the need to safeguard sensitive participant information and the
cumbersome paperwork involved in data sharing. In this article, we first
provide a selective review of recent methodological developments in information
integration via empirical likelihood, wherein only summary information is
required, rather than the raw data. Following this, we introduce a new insight
and a potentially promising framework that could broaden the application of
information integration across a wider spectrum. Furthermore, this new
framework offers computational convenience compared to classic empirical
likelihood-based methods. We provide numerical evaluations to assess its
performance and discuss various extensions in the end.
