Multivariate Meta-Analysis of Heterogeneous Studies Using Only Summary Statistics: Efficiency and Robustness

Meta-analysis has been widely used to synthesize evidence from multiple studies for common hypotheses or parameters of interest. However, it has not yet been fully developed for incorporating heterogeneous studies, which arise often in applications due to different study designs, populations, or out...

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Bibliographic Details
Published in:Journal of the American Statistical Association Vol. 110; no. 509; pp. 326 - 340
Main Authors: Liu, Dungang, Liu, Regina Y., Xie, Minge
Format: Journal Article
Language:English
Published: United States Taylor & Francis 01.03.2015
Taylor & Francis Group, LLC
Taylor & Francis Ltd
Subjects:
Aircraft
Analysis
Analysis of covariance
aviation
Clinical research
Clinical trials
Combining information
Complex evidence synthesis
Confidence distribution
Covariance
Data
data analysis
equations
Generalized estimating equations
Heterogeneity
Hypotheses
Indirect evidence
Individual differences
Individual participant data
Inference
Maximum likelihood method
Meta-analysis
Multivariate analysis
Parameter estimation
Property
randomized clinical trials
Robustness
Statistical inference
Statistics
Theory and Methods
individual participant data
multivariate meta-analysis
efficiency
combining information
generalized estimating equations
indirect evidence
confidence distribution
complex evidence synthesis
heterogeneous studies
ISSN:1537-274X, 0162-1459, 1537-274X
Online Access:Get full text
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Summary:Meta-analysis has been widely used to synthesize evidence from multiple studies for common hypotheses or parameters of interest. However, it has not yet been fully developed for incorporating heterogeneous studies, which arise often in applications due to different study designs, populations, or outcomes. For heterogeneous studies, the parameter of interest may not be estimable for certain studies, and in such a case, these studies are typically excluded from conventional meta-analysis. The exclusion of part of the studies can lead to a nonnegligible loss of information. This article introduces a meta-analysis for heterogeneous studies by combining the confidence density functions derived from the summary statistics of individual studies, hence referred to as the CD approach. It includes all the studies in the analysis and makes use of all information, direct as well as indirect. Under a general likelihood inference framework, this new approach is shown to have several desirable properties, including: (i) it is asymptotically as efficient as the maximum likelihood approach using individual participant data (IPD) from all studies; (ii) unlike the IPD analysis, it suffices to use summary statistics to carry out the CD approach. Individual-level data are not required; and (iii) it is robust against misspecification of the working covariance structure of parameter estimates. Besides its own theoretical significance, the last property also substantially broadens the applicability of the CD approach. All the properties of the CD approach are further confirmed by data simulated from a randomized clinical trials setting as well as by real data on aircraft landing performance. Overall, one obtains a unifying approach for combining summary statistics, subsuming many of the existing meta-analysis methods as special cases.
Bibliography:http://dx.doi.org/10.1080/01621459.2014.899235
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Dungang Liu is Postdoctoral Associate, Department of Biostatistics, Yale University School of Public Health, New Haven, CT 06511. dungang.liu@yale.edu. Regina Liu and Minge Xie are Professors, Department of Statistics and Biostatistics, Rutgers University, Piscataway, NJ 08854. rliu; mxie@stat.rutgers.edu.
ISSN:1537-274X
0162-1459
1537-274X
DOI:10.1080/01621459.2014.899235