Literature DB >> 20703369

Measuring Study-Specific Heterogeneity in Meta-Analysis: Application to an Antecedent Biomarker Study of Alzheimer's Disease.

Chengjie Xiong1, J Philip Miller, John C Morris.   

Abstract

This article proposes several new indices that measure the heterogeneity for individual studies in a meta-analysis. These indices directly assess how inconsistent an individual study is compared to the rest of studies used in the meta-analysis, that is, how much impact the specific study has on the scientific conclusion of the meta-analysis and further on the generalization of the conclusion. The proposed indices can be intuitively interpreted as the proportion of total variance from all studies in a meta-analysis that can be accounted for by the heterogeneity from specific studies. Further, each proposed index over all the studies sums to the collective measure of heterogeneity for the meta-analysis. Therefore our proposed study-specific indices of heterogeneity can be regarded as a generalization of the collective index of heterogeneity in meta-analyses proposed by various authors. We examine the difference among the proposed study-specific measures of heterogeneity and assess the variation associated with each proposed index of heterogeneity through a large simulation study. Finally, we demonstrate the proposed methodology by assessing the effect of individual studies on the overall estimate to the difference of an antecedent biomarker of Alzheimer's disease (AD) between different Apolipoprotein E (ApoE) genotypes.

Entities:  

Year:  2010        PMID: 20703369      PMCID: PMC2919159          DOI: 10.1198/sbr.2009.0067

Source DB:  PubMed          Journal:  Stat Biopharm Res        ISSN: 1946-6315            Impact factor:   1.452


  26 in total

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Authors:  N Andreasen; C Hesse; P Davidsson; L Minthon; A Wallin; B Winblad; H Vanderstichele; E Vanmechelen; K Blennow
Journal:  Arch Neurol       Date:  1999-06

2.  Cerebrospinal beta-amyloid ((1-42)) in early Alzheimer's disease: association with apolipoprotein E genotype and cognitive decline.

Authors:  M Riemenschneider; M Schmolke; N Lautenschlager; W G Guder; H Vanderstichele; E Vanmechelen; A Kurz
Journal:  Neurosci Lett       Date:  2000-04-21       Impact factor: 3.046

3.  Quantifying heterogeneity in a meta-analysis.

Authors:  Julian P T Higgins; Simon G Thompson
Journal:  Stat Med       Date:  2002-06-15       Impact factor: 2.373

4.  Extracting summary statistics to perform meta-analyses of the published literature for survival endpoints.

Authors:  M K Parmar; V Torri; L Stewart
Journal:  Stat Med       Date:  1998-12-30       Impact factor: 2.373

Review 5.  Meta-Analysis. Potentials and promise.

Authors:  M Egger; G D Smith
Journal:  BMJ       Date:  1997-11-22

6.  Detecting and describing heterogeneity in meta-analysis.

Authors:  R J Hardy; S G Thompson
Journal:  Stat Med       Date:  1998-04-30       Impact factor: 2.373

7.  Cerebrospinal fluid tau/beta-amyloid(42) ratio as a prediction of cognitive decline in nondemented older adults.

Authors:  Anne M Fagan; Catherine M Roe; Chengjie Xiong; Mark A Mintun; John C Morris; David M Holtzman
Journal:  Arch Neurol       Date:  2007-01-08

8.  Meta-analysis in clinical trials.

Authors:  R DerSimonian; N Laird
Journal:  Control Clin Trials       Date:  1986-09

9.  Decreased beta-amyloid1-42 and increased tau levels in cerebrospinal fluid of patients with Alzheimer disease.

Authors:  Trey Sunderland; Gary Linker; Nadeem Mirza; Karen T Putnam; David L Friedman; Lida H Kimmel; Judy Bergeson; Guy J Manetti; Matthew Zimmermann; Brian Tang; John J Bartko; Robert M Cohen
Journal:  JAMA       Date:  2003 Apr 23-30       Impact factor: 56.272

10.  Apolipoprotein E epsilon4 association with dementia in a population-based study: The Framingham study.

Authors:  R H Myers; E J Schaefer; P W Wilson; R D'Agostino; J M Ordovas; A Espino; R Au; R F White; J E Knoefel; J L Cobb; K A McNulty; A Beiser; P A Wolf
Journal:  Neurology       Date:  1996-03       Impact factor: 9.910

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  2 in total

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2.  metaSEM: an R package for meta-analysis using structural equation modeling.

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