<?xml version="1.0" encoding="UTF-8"?>
<CRANTaskView>

<name>MetaAnalysis</name>
<topic>Meta-Analysis</topic>
<maintainer email = "lists@dewey.myzen.co.uk">Michael Dewey</maintainer>
<version>2016-05-13</version>

<info>
<p>
   This task view covers packages which include
   facilities for meta-analysis
   of summary statistics from primary studies.
   The task view does not consider
   the meta-analysis of individual participant data (IPD)
   which can be handled by
   any of the standard linear modelling functions
   but does include some
   packages which offer special facilities for IPD.
</p>

<p>
  The standard meta-analysis model is a form of
  weighted least squares and so
  any of the wide range of R packages providing
  weighted least squares would
  in principle be able to fit the model.
  The advantage of using a specialised package is
  that (a) it takes care of the small tweaks necessary
  (b) it provides a range
  of ancillary functions for displaying
  and investigating the model.
  Where the model is referred to below it is this
  model which is meant.
</p>

<p>
  Where summary statistics are not available
  a meta-analysis of significance
  levels is possible.
  This is not completely unconnected with the problem
  of adjustment for multiple comparisons but
  the packages below which offer this,
  chiefly in the context of genetic data,
  also offer additional functionality.
</p>

<h4>Univariate meta-analysis</h4>
  <p><i>Preparing for meta-analysis</i></p>
  <ul>
  <li>The primary studies often use a range of
    statistics to present their
    results.
    Convenience functions to convert these onto a common
    metric are presented by:
    <pkg>compute.es</pkg> which converts from
    various statistics to
    d, g, r, z and the log odds ratio,
    <pkg>MAc</pkg> which converts to correlation coefficients,
    <pkg>MAd</pkg> which converts to mean differences,
    and
    <pkg>metafor</pkg> which converts to effect sizes
    an extensive set of measures
    for comparative studies (such as binary data,
    person years, mean differences and
    ratios and so on), for studies of association
    (a wide range of correlation types), for non-comparative
    studies (proportions, incidence rates, and mean change).
    It also provides for a measure 
    used in psychometrics (Cronbach's alpha).
  </li>
  <li>
    <pkg>meta</pkg> provides functions to read and work
    with files output by RevMan 4 and 5.
  </li>
  <li>
    <pkg>metagear</pkg> provides an extensive range of
    facilities to support the systematic review process,
    ease data extraction, and generate effect sizes.
  </li>
  </ul>

  <p><i>Fitting the model</i></p>
  <ul>
  <li>
    Four packages provide the inverse variance weighted,
    Mantel-Haenszel,
    and Peto methods:  <pkg>epiR</pkg>,
    <pkg>meta</pkg>, <pkg>metafor</pkg>, and <pkg>rmeta</pkg>.
  </li>
  <li>
    For binary data <pkg>metafor</pkg> provides
    the binomial-normal model.
  </li>
  <li>
    For sparse binary data <pkg>exactmeta</pkg>
    provides an exact method which
    does not involve continuity corrections.
  </li>
  <li>Packages which work with specific effect sizes
    may be more congenial
    to workers in some areas of science
    <pkg>MAc</pkg> and
    <pkg>metacor</pkg>
    which provide meta-analysis of correlation
    coefficients and 
    <pkg>MAd</pkg> which provides meta-analysis
    of mean differences.
    <pkg>MAc</pkg> and <pkg>MAd</pkg> provide
    a range of graphics.
    <pkg>psychometric</pkg>
    provides an extensive range of functions
    for the meta-analysis of psychometric studies.
  </li>
  <li>
    Bayesian approaches are contained in various packages.
    <pkg>bspmma</pkg> which 
    provides two different models:
    a non-parametric and a semi-parametric.
    Graphical display of the results is provided.
    <pkg>metamisc</pkg> provides a method
    with priors suggested by Higgins.
    <pkg>mmeta</pkg> provides meta-analysis using
    beta-binomial prior distributions.
   A Bayesian approach is also provided by <pkg>bmeta</pkg> which
   provides forest plots via
   <pkg>forestplot</pkg>
   and diagnostic graphical output.
   <pkg>bayesmeta</pkg> also provides a Bayesian approach
   with forest plots via <pkg>metafor</pkg>
   and diagnostic graphical output.
  </li>
  <li>
    Some packages concentrate on providing
    a specialised version of the core
    meta-analysis function without providing
    the range of ancillary
    functions. These are:
   <pkg>gmeta</pkg>
   which subsumes a very wide variety of models under the method
   of confidence distributions and
   also provides a graphical display,
    <pkg>metaLik</pkg>
    which uses a more sophisticated approach
    to the likelihood,
    <pkg>metamisc</pkg> which as well as the
    method of moments provides
    two likelihood-based  methods, and
    <pkg>metatest</pkg> which provides
    another improved method of obtaining confidence intervals.
  </li>
  <li><pkg>metagen</pkg> provides a range of methods for
    random effects models and also facilities
    for extensive simulation studies of the
    properties of those methods.
  </li>
  <li>
    <pkg>metaplus</pkg> fits random effects
    models relaxing the usual
    assumption that the random effects have a normal
    distribution. Also provides some diagnostics.
  </li>
  </ul>

<i>Graphical methods</i>
  <p>
    An extensive range of graphical procedures is available.
  </p>
  <ul>
  <li>
    Forest plots are provided in <pkg>forestplot</pkg>,
    <pkg>meta</pkg>, <pkg>metafor</pkg> and <pkg>rmeta</pkg>.
    Although the most basic plot can be produced
    by any of them
    they each provide their own choice of enhancements.
  </li>
  <li>
    Funnel plots are provided in
    <pkg>meta</pkg>, <pkg>metafor</pkg> and <pkg>rmeta</pkg>.
    In addition to the standard funnel plots
    an enhanced funnel plot to assess the
    impact of extra evidence
    is available in <pkg>extfunnel</pkg>, and a funnel plot
    for limit meta-analysis in
    <pkg>metasens</pkg>,
  </li>
  <li>
    Radial (Galbraith) plots are provided in
    <pkg>metafor</pkg>,
  </li>
  <li>
    L'Abbe plots are provided in
    <pkg>meta</pkg> and <pkg>metafor</pkg>,
  </li>
  <li>
    Baujat plots are provided in
    <pkg>meta</pkg> and <pkg>metafor</pkg>
  </li>
  <li>
    An extensive series of plots of diagnostic statistics is
    provided in <pkg>metafor</pkg>.
  </li>
  </ul>

<i>Investigating heterogeneity</i>
  <ul>
  <li>
    Confidence intervals for the heterogeneity parameter
    are provided in <pkg>metafor</pkg>
    and <pkg>metagen</pkg>.
  </li>
  <li>
  <pkg>altmeta</pkg>
  presents a variety of alternative methods for measuring
  and testing heterogeneity with a focus on robustness
  to outlying studies.
  </li>
  <li>
  <pkg>hetmeta</pkg>
  calculates some extra measures of heterogeneity.
  </li>
  </ul>


<i>Investigating small study bias</i>
  <p>The issue of whether small studies give different results
    from large studies has been addressed by visual
    examination of  the funnel plots mentioned above.
    In addition:
  </p>
  <ul>
  <li> <pkg>meta</pkg> and <pkg>metafor</pkg> provide
    both the non-parametric method suggested
    by Begg and Mazumdar
    and a range of regression tests modelled
    after the approach of Egger.
  </li>
  <li>
  <pkg>xmeta</pkg> provides a method in the context of
  multivariate meta-analysis.
  </li>
  <li>
    An exploratory technique for detecting
    an excess of statistically
    significant studies is provided by <pkg>PubBias</pkg>.
  </li>
  </ul>

<i>Unobserved studies</i>
  <p>
    A recurrent issue in meta-analysis has been
    the problem of unobserved studies.
  </p>
  <ul>
  <li>
    Rosenthal's fail safe n is provided by
    <pkg>MAc</pkg> and <pkg>MAd</pkg>.
    <pkg>metafor</pkg> provides it as well as two
    more recent methods  by Orwin and Rosenberg.
  </li>
  <li>
    Duval's trim and fill method is provided
    by <pkg>meta</pkg> and <pkg>metafor</pkg>.
  </li>
  <li>
    <pkg>metasens</pkg> provides Copas's selection
    model and also
    the method of limit meta-analysis (a regression based
    approach for dealing with small study effects)
    due to R??cker et al.
  </li>
  <li>
    <pkg>selectMeta</pkg> provides various selection models:
    the parametric model of Iyengar and Greenhouse,
    the non-parametric model of Dear and Begg, and
    proposes a new non-parametric method imposing a 
    monotonicity constraint.
  </li>
  <li>
    <pkg>SAMURAI</pkg> performs a sensitivity
    analysis assuming
    the number of unobserved studies is known,
    perhaps from a trial registry, but not their outcome.
  </li>
  <li>
   The <pkg>metansue</pkg> package allows the inclusion
   by multiple imputation
   of studies known only to have a non-significant
   result.
  </li>
  <li>
  <pkg>weightr</pkg>
  provides
  facilities for using the weight function model
  of Vevea and Hedges.
  </li>
  </ul>

<i>Meta-analysis of significance values</i>
  <p>
    <pkg>metap</pkg> provides some facilities for
    meta-analysis of significance values.
    Some of these methods are also provided in some
    of the genetics packages mentioned below.
  </p>

<h4>Multivariate meta-analysis</h4>

  <p>Standard methods outlined above assume that
    the effect sizes are independent.
    This assumption may be violated in a number of ways:
    within each primary study multiple treatments may
    be compared to the same control,
    each primary study may report multiple
    endpoints, or primary studies may be clustered
    for instance because they come from
    the same country or the same research team.
    In these situations where the outcome is multivariate:
  </p>
  <ul>
  <li>
    <pkg>mvmeta</pkg> assumes the within study covariances
    are known and as well as fixed effects provides a
    variety of options for fitting random effects.
    <pkg>metafor</pkg>
    provides fixed effects and likelihood
    based random effects model fitting procedures.
    Both these packages include meta-regression,
    <pkg>metafor</pkg> also provides for clustered and
    hierarchical models
  </li>
  <li>
    <pkg>mvtmeta</pkg> provides multivariate meta-analysis 
    using the method of moments for random effects
    although not meta-regression,
  </li>
  <li>
    <pkg>metaSEM</pkg> provides multivariate
    (and univariate) meta-analysis and
    meta-regression by embedding it in the
    structural equation framework
    and using OpenMx for the structural equation modelling.
    It can provide a three-level meta-analysis
    taking account of clustering and allowing for
    level 2 and level 3 heterogeneity.
    It also provides via a two-stage approach
    meta-analysis of correlation or covariance matrices.
  </li>
  <li>
    <pkg>xmeta</pkg>
      provides various functions for multivariate meta-analysis
      and also for detecting publication bias.
  </li>
  <li>
    <pkg>dosresmeta</pkg> concentrates on the situation
    where individual studies have information on
    the dose-response relationship.
  </li>
  <li>
    <pkg>robumeta</pkg> provides robust variance
    estimation for clustered and hierarchical estimates.
  </li>
  </ul>

<h4>Meta-analysis of studies of diagnostic tests</h4>
  <p>
    A special case of multivariate meta-analysis
    is the case of summarising
    studies of diagnostic tests.
    This gives rise to a bivariate, binary
    meta-analysis with the within-study correlation
    assumed zero
    although the between-study correlation is estimated.
    This is an active area of research and a variety
    of methods are available
    including what is referred to here called Reitsma's
    method, and the heirarchical summary receiver operating
    characteristic (HSROC) method.
    In many situations these are equivalent.
  </p>

  <ul>
  <li>
    <pkg>mada</pkg> provides various descriptive statistics
     and univariate methods (diagnostic odds ratio and Lehman
     model) as well as the bivariate method due to Reitsma.
     In addition meta-regression is provided.
     A range of graphical methods is also available.
  </li>
  <li>
    <pkg>HSROC</pkg> provides HSROC with estimation
    in a Bayesian framework.
    Graphical methods are provided.
    The case of imperfect reference standards is catered for.
  </li>
  <li>
    <pkg>Metatron</pkg> provides a method for
    the Reitsma model
    incuding the case of an imperfect reference standard
  </li>
  <li>
    <pkg>metamisc</pkg> provides the method
    of Riley which estimates a common
    within and between correlation.
    Graphical output is also provided.
  </li>
  <li>
    <pkg>bamdit</pkg> provides Bayesian meta-analysis
    with a bivariate random effects model
    (using JAGS to implement the MCMC method).
  </li>
  <li>
    <pkg>meta4diag</pkg>
      provides Bayesian inference analysis for bivariate meta-analysis
      of diagnostic test studies and an extensive range of
      graphical methods.
  </li>
  <li>
    <pkg>CopulaREMADA</pkg> uses a copula based mixed model
  </li>
  </ul>

<h4>Meta-regression</h4>
  <p>Where suitable moderator variables are
    available they may be included using meta-regression.
    All these packages are mentioned above, this
    just draws that information together.
  </p>
  <ul>
  <li>
    <pkg>metafor</pkg> provides meta-regression (multiple
    moderators are catered for).
    Various packages rely on <pkg>metafor</pkg> to
    provide meta-regression (<pkg>meta</pkg>, <pkg>MAc</pkg>,
    and  <pkg>MAd</pkg>) and all three of
    these provide bubble plots.
  </li>
  <li>
    <pkg>bmeta</pkg>, <pkg>metagen</pkg>,
    <pkg>metaLik</pkg>, <pkg>metaSEM</pkg>, and
    <pkg>metatest</pkg> also provide meta-regression.
  </li>
  <li>
    <pkg>mvmeta</pkg> provides meta-regression
    for multivariate meta-analysis
    as do <pkg>metafor</pkg> and <pkg>metaSEM</pkg>.
  </li>
  <li>
    <pkg>mada</pkg> provides for the
    meta-regression of diagnostic test studies.
  </li>
  </ul>

<h4>Individual participant data (IPD)</h4>

  <p>
    Where all studies can provide individual participant data
    then software for analysis of multi--centre trials
    or multi-centre cohort studies should prove adequate
    and is outside the scope of this task view.
    Other packages which provide facilities
    related to IPD are:
  </p>
  <ul>
  <li>
    <pkg>ipdmeta</pkg> which uses information on aggregate
    summary statistics and a covariate of interest
    to assess whether a full IPD analysis
    would have more power.
  </li>
  <li>
    <pkg>ecoreg</pkg> which is designed for ecological studies
    enables estimation of an individual level
    logistic regression from aggregate data or
    individual data.
   </li>
   </ul>

<h4>Network meta-analysis</h4>
  <p>Also known as multiple treatment comparison.
    This is a very active area of research and development.
    Note that some of the packages mentioned above
    under multivariate meta-analysis can also be
    used for network meta-analysis with
   appropriate setup.
  </p>

  <p>This is provided in a Bayesian framework by
    <pkg>gemtc</pkg>,
    which acts as a front-end to BUGS
    or JAGS, and <pkg>pcnetmeta</pkg>,
    which uses JAGS.
    <pkg>netmeta</pkg>  works in a frequentist framework.
    Both <pkg>pcnetmeta</pkg> and <pkg>netmeta</pkg>
    provide network graphs and
    <pkg>netmeta</pkg> provides a heatmap for
    displaying inconsistency and heterogeneity
  </p>

<h4>Genetics</h4>
  <p>
    There are a number of packages specialising
    in genetic data:
    <pkg>EasyStrata</pkg> for startified GWAS meta-analysis
      with graphics,
    <pkg>etma</pkg>
      proposes a new statistical method to detect epistasis,
    <pkg>gap</pkg> combines p-values,
    <pkg>MAMA</pkg> provides meta-analysis of microarray data,
    <pkg>MetABEL</pkg> provides meta-analysis of
      genome wide SNP association results,
    <pkg>MetaDE</pkg> provides microarray meta-analysis
      of differentially expressed dene detection,
    <pkg>metaMA</pkg> provides meta-analysis of
      p-values or moderated
     effect sizes to find differentially expressed genes,
    <pkg>MetaPath</pkg>
     performs meta-analysis for pathway enrichment, 
    <pkg>MetaPCA</pkg> provides meta-analysis in
      the dimension reduction of genomic data,
    <pkg>MetaQC</pkg> provides objective quality control and
      inclusion/exclusion criteria for genomic meta-analysis,
    <pkg>metaRNASeq</pkg> meta-analysis from multiple RNA
      sequencing experiments,
    <pkg>MultiMeta</pkg> for meta-analysis
      of multivariate GWAS
      results with graphics, designed to accept GEMMA format,
    <pkg>MetaSKAT</pkg>, <pkg>seqMeta</pkg>, and
    <pkg>skatMeta</pkg> provide meta-analysis
      for the SKAT test.
  </p>
<h4>Others</h4>

  <p>
    <pkg>SCMA</pkg> provides single case meta-analysis.
    It is part of a suite of packages
    dedicated to single-case designs.
  </p>

  <p>
    <pkg>joint.Cox</pkg> provides facilities for
	the meta-analysis of studies of joint time-to-event
	and disease progression.
  </p>
  <p>
    <pkg>CRTSize</pkg>
    provides meta-analysis as part of a package
    primarily dedicated to the determination
    of sample size in cluster randomised trials in
    particular by simulating adding a new study to the
    meta-analysis.
  </p>
  <p>
    <pkg>CAMAN</pkg> offers the possibility of
    using finite semiparametric mixtures as an
    alternative to the random effects model
    where there is heterogeneity.
    Covariates can be included to provide meta-regression.
  </p>
  <p>
    <pkg>RcmdrPlugin.EZR</pkg> provides an interface
    via the Rcmdr GUI
    using <pkg>meta</pkg> and <pkg>metatest</pkg>
    to do the heavy lifting,
    <pkg>RcmdrPlugin.RMTCJags</pkg> provides an interface
    for network meta-analysis using BUGS code,
    and <pkg>MAVIS</pkg> provides a shiny
    interface using <pkg>metafor</pkg>, <pkg>MAc</pkg> and
    <pkg>MAd</pkg>.
  </p>

 </info>

 <packagelist>
  <pkg>altmeta</pkg>
 <pkg>bamdit</pkg>
 <pkg>bayesmeta</pkg>
 <pkg>bmeta</pkg>
  <pkg>bspmma</pkg>
  <pkg>CAMAN</pkg>
  <pkg>compute.es</pkg>
  <pkg>CopulaREMADA</pkg>
  <pkg>CRTSize</pkg>       
  <pkg>dosresmeta</pkg>
  <pkg>EasyStrata</pkg>
   <pkg>ecoreg</pkg>
  <pkg>epiR</pkg>
  <pkg>etma</pkg>
  <pkg>exactmeta</pkg>
  <pkg>extfunnel</pkg>
  <pkg>forestplot</pkg>
  <pkg>gap</pkg>
  <pkg>gemtc</pkg>
  <pkg>gmeta</pkg>
  <pkg>hetmeta</pkg>
  <pkg>HSROC</pkg>
  <pkg>ipdmeta</pkg>
  <pkg>joint.Cox</pkg>
  <pkg>MAc</pkg>
  <pkg>MAd</pkg>
  <pkg>mada</pkg>
  <pkg>MAMA</pkg>
  <pkg>MAVIS</pkg>
  <pkg priority="core">meta</pkg>
  <pkg>MetABEL</pkg>
 <pkg>metansue</pkg>
  <pkg>meta4diag</pkg>
  <pkg>metacor</pkg>
  <pkg>MetaDE</pkg>
  <pkg priority="core">metafor</pkg>
  <pkg>metagear</pkg>
  <pkg>metagen</pkg>
  <pkg>metaLik</pkg>
  <pkg>metaMA</pkg>
  <pkg>metamisc</pkg>
  <pkg>metap</pkg>
  <pkg>MetaPath</pkg>
  <pkg>MetaPCA</pkg>
  <pkg>metaplus</pkg>
  <pkg>MetaQC</pkg>
  <pkg>metaRNASeq</pkg>
  <pkg>metaSEM</pkg>
  <pkg>metasens</pkg>
  <pkg>MetaSKAT</pkg>
  <pkg>metatest</pkg>
  <pkg>Metatron</pkg>
  <pkg>mmeta</pkg>
  <pkg>MultiMeta</pkg>
  <pkg>mvmeta</pkg>
  <pkg>mvtmeta</pkg>
  <pkg>netmeta</pkg>
  <pkg>pcnetmeta</pkg>
  <pkg>psychometric</pkg>
  <pkg>PubBias</pkg>
  <pkg>RcmdrPlugin.EZR</pkg>
  <pkg>RcmdrPlugin.RMTCJags</pkg>
  <pkg>rmeta</pkg>
  <pkg>robumeta</pkg>
  <pkg>SAMURAI</pkg>
  <pkg>SCMA</pkg>
  <pkg>selectMeta</pkg>
  <pkg>seqMeta</pkg>
  <pkg>skatMeta</pkg>
  <pkg>weightr</pkg>
  <pkg>xmeta</pkg>
 </packagelist>

 <links>
  <view>Genetics</view>
  <view>ClinicalTrials</view>
 </links>

</CRANTaskView>

