Online Program Home
  My Program

All Times EDT

Abstract Details

Activity Number: 162 - Recent Development in Data Fusion
Type: Topic Contributed
Date/Time: Tuesday, August 4, 2020 : 10:00 AM to 11:50 AM
Sponsor: Section on Bayesian Statistical Science
Abstract #309671
Title: Data Fusion Using Summary Versus Individual Data: Relative Efficiency for Random-Effects Models
Author(s): Dungang Liu* and Ding-Geng Chen and Xiaoyi Min and Heping Zhang
Companies: University of Cincinnati and The University of North Carolina Chapel Hill and Georgia State University and Yale University
Keywords: Divide and conquer; evidence synthesis; IPD; literature review; one-stage IPD; two-stage IPD
Abstract:

Data fusion is a process of combining information from diverse sources so that a more reliable and efficient conclusion can be reached. It can be conducted by either integrating study-level summary statistics or drawing inference from an overarching model for individual participant data (IPD) if available. The latter is often viewed as the “gold standard”. For random-effects models, however, it remains not fully understood whether the use of IPD indeed gains efficiency over summary statistics. In this paper, we examine the relative efficiency of the two methods under a general likelihood inference setting. We show theoretically and numerically that summary-statistics-based analysis is at most as efficient as IPD analysis, provided that the random effects follow the Gaussian distribution and maximum likelihood estimation is used to obtain summary statistics. More specifically, (i) the two methods are equivalent in an asymptotic sense; and (ii) summary-statistics-based inference can incur an appreciable loss of efficiency if the sample sizes are not sufficiently large. Our results are established under the assumption that the between-study heterogeneity parameter remains constant reg


Authors who are presenting talks have a * after their name.

Back to the full JSM 2020 program