  In multi-center clinical trials, due to various reasons, the individual-level
data are strictly restricted to be assessed publicly. Instead, the summarized
information is widely available from published results. With the advance of
computational technology, it has become very common in data analyses to run on
hundreds or thousands of machines simultaneous, with the data distributed
across those machines and no longer available in a single central location. How
to effectively assemble the summarized clinical data information or information
from each machine in parallel computation has become a challenging task for
statisticians and computer scientists. In this paper, we selectively review
some recently-developed statistical methods, including communication efficient
distributed statistical inference, and renewal estimation and incremental
inference, which can be regarded as the latest development of calibration
information methods in the era of big data. Even though those methods were
developed in different fields and in different statistical frameworks, in
principle, they are asymptotically equivalent to those well known methods
developed in meta analysis. Almost no or little information is lost compared
with the case when full data are available. As a general tool to integrate
information, we also review the generalized method of moments and estimating
equations approach by using empirical likelihood method.
