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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Vydané v:	Journal of the American Statistical Association Ročník 110; číslo 509; s. 326 - 340
Hlavní autori:	Liu, Dungang, Liu, Regina Y., Xie, Minge
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States Taylor & Francis 01.03.2015 Taylor & Francis Group, LLC Taylor & Francis Ltd
Predmet:	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
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Abstract	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.
AbstractList	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. 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 non-negligible loss of information. This paper introduces a metaanalysis 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 the 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 an unifying approach for combining summary statistics, subsuming many of the existing meta-analysis methods as special cases. 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 non-negligible loss of information. This paper introduces a metaanalysis for heterogeneous studies by combining the 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 the 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 an unifying approach for combining summary statistics, subsuming many of the existing meta-analysis methods as special cases.
Author	Liu, Dungang Xie, Minge Liu, Regina Y.
AuthorAffiliation	1 Department of Biostatistics, Yale University School of Public Health, New Haven, CT 06511, USA 2 Department of Statistics and Biostatistics, Rutgers University, New Brunswick, NJ 08854, USA
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Notes	http://dx.doi.org/10.1080/01621459.2014.899235 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 ObjectType-Article-1 ObjectType-Feature-2 content type line 23 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.
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SubjectTerms	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
Title	Multivariate Meta-Analysis of Heterogeneous Studies Using Only Summary Statistics: Efficiency and Robustness
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