scholarly journals Gray Matter alterations in MS and CIS: a Coordinate based Meta-analysis and regression

2020 ◽  
Author(s):  
Sonika Singh ◽  
Christopher Tench ◽  
Radu Tanasescu ◽  
Cris Constantinescu
Keyword(s):  
Grey Matter ◽  
Disease Duration ◽  
Type I Error ◽  
Meta Analysis ◽  
Random Effect ◽  
Clinically Isolated Syndrome ◽  
Type I ◽  
Z Score ◽  
Analysis Method ◽  
Voxel Based Morphometry

AbstractThe purpose of this coordinate based meta-analysis (CBMA) was to summarise the available evidence related to regional grey matter (GM) changes in patients with multiple sclerosis (MS) and clinically isolated syndrome (CIS). CBMA is a way to find the consistent results across multiple independent studies that are otherwise not easily comparable due to methodological differences. The coordinate based random effect size (CBRES) meta-analysis method utilizes the reported coordinates (foci of the clusters of GM loss) and Z score standardised by number of subjects, controlling type I error rate by false cluster discovery rate (FCDR). Thirty-four published articles reporting forty-five independent studies using voxel-based morphometry (VBM) for the assessment of GM atrophy between MS or CIS patients and healthy controls were identified from electronic databases. The primary meta-analysis identified clusters of spatially consistent cross-study reporting of GM atrophy; subgroup analyses and meta-regression were also performed. This meta-analysis demonstrates consistent areas of GM loss in MS or CIS, in the form of significant clusters. Some clusters also demonstrate correlation with disease duration.

scholarly journals Localised Grey Matter Atrophy in Multiple Sclerosis and Clinically Isolated Syndrome—A Coordinate-Based Meta-Analysis, Meta-Analysis of Networks, and Meta-Regression of Voxel-Based Morphometry Studies

2020 ◽  
Vol 10 (11) ◽  
pp. 798
Author(s):  
Sonika Singh ◽  
Christopher R. Tench ◽  
Radu Tanasescu ◽  
Cris S. Constantinescu
Keyword(s):  
Multiple Sclerosis ◽  
Grey Matter ◽  
Meta Analysis ◽  
Clinically Isolated Syndrome ◽  
Voxel Based Morphometry ◽  
Statistical Effect ◽  
Meta Regression ◽  
Meta Regression Analysis ◽  
Subcortical Regions ◽  
Grey Matter Atrophy

Background: Atrophy of grey matter (GM) is observed in the earliest stages of multiple sclerosis (MS) and is associated with cognitive decline and physical disability. Localised GM atrophy in MS can be explored and better understood using magnetic resonance imaging and voxel-based morphometry (VBM). However, results are difficult to interpret due to methodological differences between studies. Methods: Coordinate-based analysis is a way to find the reliably observable results across multiple independent VBM studies. This work uses coordinate-based meta-analysis, meta-analysis of networks, and meta-regression to summarise the evidence from voxel-based morphometry of regional GM hanges in patients with MS and clinically isolated syndrome (CIS), and whether these measured changes are relatable to clinical features. Results: Thirty-four published articles reporting forty-four independent experiments using VBM for the assessment of GM atrophy between MS or CIS patients and healthy controls were identified. Analysis identified eight clusters of consistent cross-study reporting of localised GM atrophy involving both cortical and subcortical regions. Meta-network analysis identified a network-like pattern indicating that GM loss occurs with some symmetry between hemispheres. Meta-regression analysis indicates a relationship between disease duration or age and the magnitude of reported statistical effect in some deep GM structures. Conclusions: These results suggest consistency in MRI-detectible regional GM loss across multiple MS studies, and the estimated effect sizes and symmetries can help design prospective studies to test specific hypotheses.

scholarly journals Coordinate based meta-analysis of whole-brain voxel-based morphometry studies does not show evidence of grey matter loss specific to PTSD

2018 ◽  
Author(s):  
Christopher R. Tench ◽  
Radu Tanasescu ◽  
Ketan D. Jethwa ◽  
Cris S. Constantinescu
Keyword(s):  
Grey Matter ◽  
Meta Analysis ◽  
Random Effect ◽  
Traumatic Experience ◽  
Voxel Based Morphometry ◽  
Post Traumatic Stress ◽  
Whole Brain ◽  
Show Evidence ◽  
Technical Issues ◽  
Post Traumatic

AbstractNeuroimaging studies have detected structural alteration in post-traumatic stress disorder (PTSD), but findings are inconsistent. This might be explained by heterogeneity between subjects with PTSD in terms of common comorbidities such as depressive and anxiety disorders and also in traumatic experience. Despite this, coordinate based meta-analysis (CBMA) has been used to try and identify localised grey matter changes, and does suggest some PTSD specific pathology. However, there are multiple technical issues that make the meta-analytic evidence questionable, warranting a re-evaluation.A literature search for voxel-based morphometry studies was performed. Only whole-brain studies using subjects with a current diagnosis of PTSD, and having a comparison group of either healthy or trauma exposed controls, were included. Twenty one voxel-based morphometry studies met the inclusion criteria. CBMA was performed to identify altered grey matter (GM) structures.Using a novel coordinate based random effect size meta-analysis, no grey matter structure was identified as being consistently altered in PTSD compared to controls. This was also verified using the activation likelihood estimate algorithm.There is no evidence, from CBMA, of consistent localised grey matter changes specific to PTSD. Inconsistency may reflect true heterogeneity in PTSD pathology or methodological issues with imaging and/or analysis, limiting the detection of PTSD specific pathology.

The Robustness of the Likelihood Ratio Chi-Square Test for Structural Equation Models: A Meta-Analysis

2001 ◽  
Vol 26 (1) ◽  
pp. 105-132 ◽  
Author(s):  
Douglas A. Powell ◽  
William D. Schafer
Keyword(s):  
Structural Equation ◽  
Structural Equation Models ◽  
Type I Error ◽  
Meta Analysis ◽  
Generalized Least Squares ◽  
Error Rates ◽  
Type I ◽  
Chi Square ◽  
Distribution Free ◽  
Projection Techniques

The robustness literature for the structural equation model was synthesized following the method of Harwell which employs meta-analysis as developed by Hedges and Vevea. The study focused on the explanation of empirical Type I error rates for six principal classes of estimators: two that assume multivariate normality (maximum likelihood and generalized least squares), elliptical estimators, two distribution-free estimators (asymptotic and others), and latent projection. Generally, the chi-square tests for overall model fit were found to be sensitive to non-normality and the size of the model for all estimators (with the possible exception of the elliptical estimators with respect to model size and the latent projection techniques with respect to non-normality). The asymptotic distribution-free (ADF) and latent projection techniques were also found to be sensitive to sample sizes. Distribution-free methods other than ADF showed, in general, much less sensitivity to all factors considered.

scholarly journals Cluster Wild Bootstrapping to Handle Dependent Effect Sizes in Meta-Analysis with a Small Number of Studies

2021 ◽  
Author(s):  
Megha Joshi ◽  
James E Pustejovsky ◽  
S. Natasha Beretvas
Keyword(s):  
Effect Size ◽  
Type I Error ◽  
Meta Analysis ◽  
Error Rates ◽  
Small Sample ◽  
Type I ◽  
Hypothesis Tests ◽  
Type I Error Rates ◽  
Meta Analyses ◽  
Small Sample Correction

The most common and well-known meta-regression models work under the assumption that there is only one effect size estimate per study and that the estimates are independent. However, meta-analytic reviews of social science research often include multiple effect size estimates per primary study, leading to dependence in the estimates. Some meta-analyses also include multiple studies conducted by the same lab or investigator, creating another potential source of dependence. An increasingly popular method to handle dependence is robust variance estimation (RVE), but this method can result in inflated Type I error rates when the number of studies is small. Small-sample correction methods for RVE have been shown to control Type I error rates adequately but may be overly conservative, especially for tests of multiple-contrast hypotheses. We evaluated an alternative method for handling dependence, cluster wild bootstrapping, which has been examined in the econometrics literature but not in the context of meta-analysis. Results from two simulation studies indicate that cluster wild bootstrapping maintains adequate Type I error rates and provides more power than extant small sample correction methods, particularly for multiple-contrast hypothesis tests. We recommend using cluster wild bootstrapping to conduct hypothesis tests for meta-analyses with a small number of studies. We have also created an R package that implements such tests.

scholarly journals A Likelihood Ratio Test For The Homogeneity of Between-Study Variance in Network Meta-Analysis

2021 ◽  
Author(s):  
Dapeng Hu ◽  
Chong Wang ◽  
Annette O'Connor
Keyword(s):  
Likelihood Ratio ◽  
Likelihood Ratio Test ◽  
Random Effects ◽  
Type I Error ◽  
Meta Analysis ◽  
Indirect Evidence ◽  
Point Estimate ◽  
Ratio Test ◽  
Type I ◽  
Statistical Heterogeneity

Abstract Background: Network meta-analysis (NMA) is a statistical method used to combine results from several clinical trials and simultaneously compare multiple treatments using direct and indirect evidence. Statistical heterogeneity is a characteristic describing the variability in the intervention effects being evaluated in the different studies in network meta-analysis. One approach to dealing with statistical heterogeneity is to perform a random effects network meta-analysis that incorporates a between-study variance into the statistical model. A common assumption in the random effects model for network meta-analysis is the homogeneity of between-study variance across all interventions. However, there are applications of NMA where the single between-study assumption is potentially incorrect and instead the model should incorporate more than one between-study variances. Methods: In this paper, we develop an approach to testing the homogeneity of between-study variance assumption based on a likelihood ratio test. A simulation study was conducted to assess the type I error and power of the proposed test. This method is then applied to a network meta-analysis of antibiotic treatments for Bovine respiratory disease (BRD). Results: The type I error rate was well controlled in the Monte Carlo simulation. The homogeneous between-study variance assumption is unrealistic both statistically and practically in the network meta-analysis BRD. The point estimate and conffdence interval of relative effect sizes are strongly inuenced by this assumption. Conclusions: Since homogeneous between-study variance assumption is a strong assumption, it is crucial to test the validity of this assumption before conducting a network meta-analysis. Here we propose and validate a method for testing this single between-study variance assumption which is widely used for many NMA.

Grey matter abnormalities in first‐episode mania: A systematic review and meta‐analysis of voxel‐based morphometry studies

2020 ◽  
Author(s):  
Kamyar Keramatian ◽  
Trisha Chakrabarty ◽  
Gayatri Saraf ◽  
Jairo V. Pinto ◽  
Lakshmi N. Yatham
Keyword(s):  
Systematic Review ◽  
Grey Matter ◽  
Meta Analysis ◽  
First Episode ◽  
Voxel Based Morphometry

Misspecification of the random effect structure: Implications for the linear mixed model

2020 ◽  
Author(s):  
Brandon LeBeau
Keyword(s):  
Fixed Effects ◽  
Serial Correlation ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Type I Error ◽  
Random Effect ◽  
Correlated Data ◽  
Unbiased Estimation ◽  
Type I ◽  
Nested Data

<p>The linear mixed model is a commonly used model for longitudinal or nested data due to its ability to account for the dependency of nested data. Researchers typically rely on the random effects to adequately account for the dependency due to correlated data, however serial correlation can also be used. If the random effect structure is misspecified (perhaps due to convergence problems), can the addition of serial correlation overcome this misspecification and allow for unbiased estimation and accurate inferences? This study explored this question with a simulation. Simulation results show that the fixed effects are unbiased, however inflation of the empirical type I error rate occurs when a random effect is missing from the model. Implications for applied researchers are discussed.</p>

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