Metasem: An R package for Meta-Analysis using structural equation modelling - Pubrica

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METASEM AN R PACKAGE FOR META-ANALYSIS USING
STRUCTURAL EQUATION MODELLING
An Academic presentation by Dr. Nancy Agens,
Head, Technical Operations, Pubrica
Group www.pubrica.com Email sales_at_pubrica.com
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Today's Discussion
OUTLINE OF TOPICS In brief Introduction SEM
(Structural Equation Modelling) Structural
Equation Modelling Based Meta Analysis
Univariate Fixed-Effects Model
Univariate Random-Effects Model Univariate
Mixed-Effects Model Multivariate Meta-Analysis
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In brief

• SEM are used meta-analytical model formulated for
  conducting Meta-analysis which is used to
  analyse structural relationships. SEM can be
  univariate, multivariate, and three-level
  meta-analysis. Structural equation model (SEM)
  in general optimized and fit by using OpenMx
  package. The routine analysis of batch mode
  either interactively or noninteractively can be
  analysed by R package. Using the graphical
  interface like R studio is convenient method for
  users to interfere the analysis.

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Introduction

• A methodological tool used for comparing the data
  of the studies obtained between two groups was
  Meta- analysis.
• SEM is A method used for analysing longitudinal
  data.
• A collection of functions via., R statistical
  platform accessed by OpenMx package for
  conducting meta-analysis using SEM is the metaSEM
  package.
• Meta-analysis can be conducted by various
  unrelated programs for performing research in
  scientific and social studies.

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SEM (Structural Equation Modelling)
The relation among measured variables and latent
constructs in the aspect of structural can be
analysed using SEM.
It also possesses the techniques like path and
factor analysis, regression and latent growth
curve modelling for solving linear equations.
It is a single analysis technique used for
estimating interrelated dependence and multiple
factors. Endo and exogenous variables can be
used simultaneously in SEM.
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Structural Equation Modelling Based Meta Analysis
A hypothesis can be tested and fit with the
multivariate technique using the SEM model. It
is postulated that the model for the first which
includes the vector of parameters that can
be regression coefficients, error variances,
factor loadings, and factor variances. The model
is µµ(?) and SS(?) where µ and S are the
vector of mean population and covariance
matrix. The most common method for estimating
method in SEM is Maximum likelihood (ML)
estimation method. The -2log-likelihood (-2LL)
for the ith case is, Contd..
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Univariate Fixed-Effects Model
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Fig. 1 Univariate Fixed-Effects Model
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Univariate Random-Effects Model
The own specific study effect can be selected for
the random-effects model in case of the
variation in the expected population size. The
model for the ith study is yißRuiei,
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Fig. 2 Univariate Random-Effects Model
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Univariate Mixed-Effects Model
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Fig. 3 Univariate Mixed-Effects Model
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Multivariate Meta-Analysis
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Fig. 4 Multivariate Meta-Analysis
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