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In multi-center clinical trials, due to various reasons, the individual-leveldata are strictly restricted to be assessed publicly. Instead, the summarizedinformation is widely available from published results. With the advance ofcomputational technology, it has become very common in data analyses to run onhundreds or thousands of machines simultaneous, with the data distributedacross those machines and no longer available in a single central location. Howto effectively assemble the summarized clinical data information or informationfrom each machine in parallel computation has become a challenging task forstatisticians and computer scientists. In this paper, we selectively reviewsome recently-developed statistical methods, including communication efficientdistributed statistical inference, and renewal estimation and incrementalinference, which can be regarded as the latest development of calibrationinformation methods in the era of big data. Even though those methods weredeveloped in different fields and in different statistical frameworks, inprinciple, they are asymptotically equivalent to those well known methodsdeveloped in meta analysis. Almost no or little information is lost comparedwith the case when full data are available. As a general tool to integrateinformation, we also review the generalized method of moments and estimatingequations approach by using empirical likelihood method.

A selective review on calibration information from similar studies based on parametric likelihood or empirical likelihood