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Meta Analysis

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Meta Analysis - Free Register to Access Experts & Abstracts

Sergi G Costafreda - One of the best experts on this subject based on the ideXlab platform.

  • parametric coordinate based Meta Analysis valid effect size Meta Analysis of studies with differing statistical thresholds
    Journal of Neuroscience Methods, 2012
    Co-Authors: Sergi G Costafreda
    Abstract:

    Abstract The aim of coordinate-based Meta-Analysis is to provide valid quantitative summaries of the literature, while taking into account the specificities of neuroimaging data. Neuroimaging findings are usually reported as coordinates of effects surviving multiple comparison correction through statistical thresholding. Different studies may use widely differing censoring thresholds, ranging from strict family-wise corrections to more lenient “uncorrected” p -values. However, standard Meta-Analysis methods do not take into account these differences, as findings from studies with varying thresholds are treated as though they were equivalent. The present paper details a development in coordinate-based Meta-Analysis which addresses this limitation. Parametric coordinate-based Meta-Analysis (PCM) computes valid estimates from thresholded measurements, integrating significant findings with the information generated by subthreshold measurements to produce asymptotically unbiased Meta-analytical summaries. The method is validated through simulated data, and demonstrated in a real data Meta-Analysis of structural differences in grey matter density in depression. PCM demonstrates a sensitivity that is comparable or superior to existing coordinate-based Meta-Analysis methods, and demonstrates high agreement between its estimates and those obtained from the Meta-Analysis of unthresholded manual volumetric measurements. PCM constitutes a powerful approach to Meta-Analysis, able to generate valid and unbiased effect-size summaries of studies with different statistical thresholds, and also allowing the integration of whole brain and region-of-interest studies.

Huseyin Naci - One of the best experts on this subject based on the ideXlab platform.

  • Is network Meta-Analysis as valid as standard pairwise Meta-Analysis? It all depends on the distribution of effect modifiers
    BMC Medicine, 2013
    Co-Authors: Jeroen P. Jansen, Huseyin Naci
    Abstract:

    Background In the last decade, network Meta-Analysis of randomized controlled trials has been introduced as an extension of pairwise Meta-Analysis. The advantage of network Meta-Analysis over standard pairwise Meta-Analysis is that it facilitates indirect comparisons of multiple interventions that have not been studied in a head-to-head fashion. Although assumptions underlying pairwise Meta-analyses are well understood, those concerning network Meta-analyses are perceived to be more complex and prone to misinterpretation.

  • Is network Meta-Analysis as valid as standard pairwise Meta-Analysis? It all depends on the distribution of effect modifiers
    LSE Research Online Documents on Economics, 2013
    Co-Authors: Jeroen P. Jansen, Huseyin Naci
    Abstract:

    Background In the last decade, network Meta-Analysis of randomized controlled trials has been introduced as an extension of pairwise Meta-Analysis. The advantage of network Meta-Analysis over standard pairwise Meta-Analysis is that it facilitates indirect comparisons of multiple interventions that have not been studied in a head-to-head fashion. Although assumptions underlying pairwise Meta-analyses are well understood, those concerning network Meta-analyses are perceived to be more complex and prone to misinterpretation. Discussion In this paper, we aim to provide a basic explanation when network Meta-Analysis is as valid as pairwise Meta-Analysis. We focus on the primary role of effect modifiers, which are study and patient characteristics associated with treatment effects. Because network Meta-Analysis includes different trials comparing different interventions, the distribution of effect modifiers cannot only vary across studies for a particular comparison (as with standard pairwise Meta-Analysis, causing heterogeneity), but also between comparisons (causing inconsistency). If there is an imbalance in the distribution of effect modifiers between different types of direct comparisons, the related indirect comparisons will be biased. If it can be assumed that this is not the case, network Meta-Analysis is as valid as pairwise Meta-Analysis. Summary The validity of network Meta-Analysis is based on the underlying assumption that there is no imbalance in the distribution of effect modifiers across the different types of direct treatment comparisons, regardless of the structure of the evidence network.

Jeroen P. Jansen - One of the best experts on this subject based on the ideXlab platform.

  • Is network Meta-Analysis as valid as standard pairwise Meta-Analysis? It all depends on the distribution of effect modifiers
    BMC Medicine, 2013
    Co-Authors: Jeroen P. Jansen, Huseyin Naci
    Abstract:

    Background In the last decade, network Meta-Analysis of randomized controlled trials has been introduced as an extension of pairwise Meta-Analysis. The advantage of network Meta-Analysis over standard pairwise Meta-Analysis is that it facilitates indirect comparisons of multiple interventions that have not been studied in a head-to-head fashion. Although assumptions underlying pairwise Meta-analyses are well understood, those concerning network Meta-analyses are perceived to be more complex and prone to misinterpretation.

  • Is network Meta-Analysis as valid as standard pairwise Meta-Analysis? It all depends on the distribution of effect modifiers
    LSE Research Online Documents on Economics, 2013
    Co-Authors: Jeroen P. Jansen, Huseyin Naci
    Abstract:

    Background In the last decade, network Meta-Analysis of randomized controlled trials has been introduced as an extension of pairwise Meta-Analysis. The advantage of network Meta-Analysis over standard pairwise Meta-Analysis is that it facilitates indirect comparisons of multiple interventions that have not been studied in a head-to-head fashion. Although assumptions underlying pairwise Meta-analyses are well understood, those concerning network Meta-analyses are perceived to be more complex and prone to misinterpretation. Discussion In this paper, we aim to provide a basic explanation when network Meta-Analysis is as valid as pairwise Meta-Analysis. We focus on the primary role of effect modifiers, which are study and patient characteristics associated with treatment effects. Because network Meta-Analysis includes different trials comparing different interventions, the distribution of effect modifiers cannot only vary across studies for a particular comparison (as with standard pairwise Meta-Analysis, causing heterogeneity), but also between comparisons (causing inconsistency). If there is an imbalance in the distribution of effect modifiers between different types of direct comparisons, the related indirect comparisons will be biased. If it can be assumed that this is not the case, network Meta-Analysis is as valid as pairwise Meta-Analysis. Summary The validity of network Meta-Analysis is based on the underlying assumption that there is no imbalance in the distribution of effect modifiers across the different types of direct treatment comparisons, regardless of the structure of the evidence network.

Aeilko H. Zwinderman - One of the best experts on this subject based on the ideXlab platform.

  • Meta-Meta-Analysis
    Modern Meta-Analysis, 2017
    Co-Authors: Ton J. Cleophas, Aeilko H. Zwinderman
    Abstract:

    A Meta-Meta-Analysis is a Meta-Analysis of multiple Meta-analyses. In this chapter a Meta-Meta-Analysis from the authors is performed for the purpose of re-assessment of the pitfalls of the original Meta-analyses with increased power and sample size. Also a Meta-Meta-Analysis, performed for Meta-learning purposes by Juni et al., and published in the BMJ of 2001, is reviewed.

  • Multivariate Meta-Analysis
    Modern Meta-Analysis, 2017
    Co-Authors: Ton J. Cleophas, Aeilko H. Zwinderman
    Abstract:

    Multivariate Analysis, simultaneously, assesses the separate effects of the predictors on one outcome adjusted for the other. E.g., it can answer clinically important questions like: does drug-compliance not only predict drug efficacy, but also, independently of the first effect, predict quality of life (qol). In this chapter (1) the effect of counseling and non-compliance on drug efficacy and quality of life was assessed in a Meta-Analysis of 25 studies, (2) the effect of type of research group on hospital admissions due to adverse drug effects, study magnitudes, and patients age, in a Meta-Analysis of 20 studies, and (3) the effect of counseling and non-compliance on drug efficacy and quality of life in a Meta-Analysis of 20 studies. In all of the examples a beneficial effect of the predictors on the multivariate outcomes was observed.

  • Meta-Analysis in a Nutshell
    Modern Meta-Analysis, 2017
    Co-Authors: Ton J. Cleophas, Aeilko H. Zwinderman
    Abstract:

    A Meta-Analysis is a systematic review with pooled outcome data. The current chapter gives a summary of methods for the purpose. Four scientific rules and three pitfalls are reviewed. The main benefit of Meta-Analysis is that it provides a pooled outcome with increased precision. The main criticisms are that so far they were not good at predicting subsequent large trials, and at predicting serious adverse effects of medicines.

Mike W L Cheung - One of the best experts on this subject based on the ideXlab platform.

  • A Guide to Conducting a Meta-Analysis.
    Neuropsychology Review, 2016
    Co-Authors: Mike W L Cheung, Ranjith Vijayakumar
    Abstract:

    Meta-Analysis is widely accepted as the preferred method to synthesize research findings in various disciplines. This paper provides an introduction to when and how to conduct a Meta-Analysis. Several practical questions, such as advantages of Meta-Analysis over conventional narrative review and the number of studies required for a Meta-Analysis, are addressed. Common Meta-analytic models are then introduced. An artificial dataset is used to illustrate how a Meta-Analysis is conducted in several software packages. The paper concludes with some common pitfalls of Meta-Analysis and their solutions. The primary goal of this paper is to provide a summary background to readers who would like to conduct their first Meta-analytic study.

  • Multivariate Meta-Analysis as Structural Equation Models
    Structural Equation Modeling, 2013
    Co-Authors: Mike W L Cheung
    Abstract:

    Multivariate Meta-Analysis has become increasingly popular in the educational, social, and medical sciences. It is because the outcome measures in a Meta-Analysis can involve more than one effect size. This article proposes 2 mathematically equivalent models to implement multivariate Meta-Analysis in structural equation modeling (SEM). Specifically, this article shows how multivariate fixed-, random- and mixed-effects Meta-analyses can be formulated as structural equation models. MetaSEM (a free R package based on OpenMx) and Mplus are used to implement the proposed procedures. A real data set is used to illustrate the procedures. Formulating multivariate Meta-Analysis as structural equation models provides many new research opportunities for methodological development in both Meta-Analysis and SEM. Issues related to and extensions on the SEM-based Meta-Analysis are discussed.

  • Meta-Analysis in medicine: an introduction.
    International Journal of Rheumatic Diseases, 2010
    Co-Authors: Anselm Mak, Mike W L Cheung
    Abstract:

    Meta-Analysis, a complex statistical method which involves synthesis of data from relevant studies to devise an effect size or a conclusion, has increasingly been recognized and impacts on evidence-based medicine, especially in the field of health science. Thanks to the advent and unmet need of evidence-based medicine, since the first recordable publication of a Meta-Analysis in 1904 addressing the effectiveness of typhoid vaccine, both the number and quality of Meta-analyses published relating to healthcare science have been on a steep rise. If properly conducted, based on answering relevant clinical questions, strict selection criteria of participating studies, appropriate analytical methods, and proper presentation of results, coupled with critical and faithful discussion on the strength and weakness of the Analysis, Meta-Analysis will definitely be an invaluable tool for clinicians and researchers in understanding epidemiology, justifying and refining hypotheses of various diseases, for medical practitioners to implement sound management decisions based on evidence-based medicine, and ultimately, for policy-makers to formulate cost-efficient treatment strategies, guidelines and legislation. In this first paper of a mini-series, the current trend of Meta-Analysis publications in the medical literature, examples of important Meta-analyses relevant to rheumatology and the pros and cons of Meta-Analysis, will be discussed. Important terminology related to Meta-Analysis, the systematic ways to critically appraise, and finally the preferred methodology of conducting Meta-Analysis will be covered in the subsequent three reviews of this mini-series.