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Multivariate and Univariate Meta-Analysis and Meta-Regression

The package consists of a collection of functions to perform various meta-analytical models, including standard univariate fixed and random-effects meta-analysis and meta-regression, and non-standard extensions such as multivariate, multilevel, longitudinal, and dose-response models. The methodology is illustrated in detail in a series of articles referenced at the end of this document.

Info on the mvmeta package

The package mvmeta is now superseded by the package mixmeta. The users are strongly suggested to replace it with the new package, as the development of mvmeta is now discontinued. For the time being, the mvmeta package is still maintained and available on the Comprehensive R Archive Network (CRAN), with info at the related web page (https://cran.r-project.org/package=mvmeta). A (discontinued) development website is available on GitHub (https://github.com/gasparrini/mvmeta).

Installation

The last version officially released on CRAN can be installed directly within R by typing:

install.packages("mvmeta")

R code in published articles

Several peer-reviewed articles and documents provide R code illustrating methodological developments of mvmeta or replicating substantive results using this package. An updated version of the code can be found at the GitHub (https://github.com/gasparrini) or personal web page (http://www.ag-myresearch.com) of the package maintainer.

References:

Gasparrini A. Multivariate meta-analysis for non-linear and other multi-parameter associations. Statistics in Medicine. 2012; 31(29):3821-3839. [freely available here]

Gasparrini A., Armstrong, B., Kenward M. G. Reducing and meta-analyzing estimates from distributed lag non-linear models. BMC Medical Research Methodology. 2013; 13(1):1. [freely available here].

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Version

Install

install.packages('mvmeta')

Monthly Downloads

1,352

Version

1.0.3

License

GPL (>= 2)

Last Published

December 10th, 2019

Functions in mvmeta (1.0.3)

coef.mvmeta

Extract Coefficients and (Co)Variance Matrix from mvmeta Objects
mvmeta.fixed

Fixed-Effects Estimator for mvmeta Models
mvmeta.control

Ancillary Parameters for Controlling the Fit in mvmeta Models
model.frame.mvmeta

Extract Model Frame and Design Matrix from mvmeta Objects
mlprof.fn

Likelihood Functions for mvmeta Models
mvmeta.mm

Method of Moments Estimator for mvmeta Models
qtest

Cochran Q Test of Heterogeneity
summary.mvmeta

Summarizing mvmeta Models
predict.mvmeta

Predicted Values from mvmeta Models
mvmeta.ml

ML and REML Estimators for mvmeta Models
vechMat

Vectorization and Expansion of Symmetric Matrices
mvmetaObject

mvmeta Objects
mvmeta.vc

Variance Components Estimator for mvmeta Models
mvmetaCovStruct

Covariance Structures for mvmeta Models
na.omit.data.frame.mvmeta

Handling Missing Values in mvmeta Models
mvmeta-package

Multivariate and Univariate Meta-Analysis and Meta-Regression
mvmetaSim

Simulating Responses for mvmeta Models
p53

Mutant p53 Gene and Squamous Cell Carcinoma
qtest.mvmeta

Cochran Q Test of Heterogeneity for mvmeta Models
smoking

Meta-Analysis of Interventions to Promote Smoking Cessation
mvmeta

Fitting Multivariate and Univariate Meta-Analysis and Meta-Regression Models
blup

Best Linear Unbiased Predictions
hsls

High School Longitudinal Study
inputna

Input Missing Values
berkey98

Five Published Trials on Periodontal Disease
hyp

Ten Studies Assessing an Hypertension Treatment
fibrinogen

Fibrinogen Studies Collaboration
blup.mvmeta

Best Linear Unbiased Predictions from mvmeta Models
inputcov

Input (Co)Variance Matrices
logLik.mvmeta

Extract Log-Likelihood from mvmeta Objects