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

TL;DR

This paper reviews recent statistical methods for combining summarized data from multiple sources or machines in big data settings, focusing on calibration techniques that preserve information quality.

Contribution

It provides a selective overview of calibration information methods based on parametric and empirical likelihood, highlighting their asymptotic equivalence to meta-analysis.

Findings

Methods are asymptotically equivalent to meta-analysis.

Little information loss compared to full data analysis.

Applicable in distributed and multi-center clinical trials.

Abstract

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.