metaSEM (version 0.9.4)

meta: Univariate and Multivariate Meta-Analysis with Maximum Likelihood Estimation

Description

It conducts univariate and multivariate meta-analysis with maximum likelihood estimation method. Mixed-effects meta-analysis can be conducted by including study characteristics as predictors. Equality constraints on intercepts, regression coefficients and variance components can be easily imposed by setting the same labels on the parameter estimates.

Usage

meta(y, v, x, data, intercept.constraints = NULL, coef.constraints = NULL,
     RE.constraints = NULL, RE.startvalues=0.1, RE.lbound = 1e-10,
     intervals.type = c("z", "LB"), I2="I2q", R2=TRUE,
     model.name="Meta analysis with ML", suppressWarnings = TRUE,
     silent = TRUE, run = TRUE, ...)

Arguments

y

A vector of effect size for univariate meta-analysis or a $k$ x $p$ matrix of effect sizes for multivariate meta-analysis where $k$ is the number of studies and $p$ is the number of effect sizes.

v

A vector of the sampling variance of the effect size for univarite meta-analysis or a $k$ x $p*$ matrix of the sampling covariance matrix of the effect sizes for multivariate meta-analysis where $p* = p(p+1)/2$. It is arranged by column major as used

x

A predictor or a $k$ x $m$ matrix of predictors where $m$ is the number of predictors.

data

An optional data frame containing the variables in the model.

intercept.constraints

A $1$ x $p$ matrix specifying whether the intercepts of the effect sizes are fixed or free. If the input is not a matrix, the input is converted into a $1$ x $p$ matrix with t(as.matrix(intercept.constraints)). The default is that the int

coef.constraints

A $p$ x $m$ matrix specifying how the predictors predict the effect sizes. If the input is not a matrix, it is converted into a matrix by as.matrix(). The default is that all $m$ predictors predict all $p$ effect sizes. The format of this

RE.constraints

A $p$ x $p$ matrix specifying the variance componets of the random effects. If the input is not a matrix, it is converted into a matrix by as.matrix(). The default is that all covariance/variance components are free. The format of this ma

RE.startvalues

A vector of $p$ starting values on the diagonals of the variance component of the random effects. If only one scalar is given, it will be duplicated across the diagonals. Starting values for the off-diagonals of the variance component are all 0. A $p$

RE.lbound

A vector of $p$ lower bounds on the diagonals of the variance component of the random effects. If only one scalar is given, it will be duplicated across the diagonals. Lower bounds for the off-diagonals of the variance component are set at NA

intervals.type

Either z (default if missing) or LB. If it is z, it calculates the 95% Wald confidence intervals (CIs) based on the z statistic. If it is LB, it calculates the 95% likelihood-based CIs on the paramet

I2

Possible options are "I2q", "I2hm" and "I2am". They represent the I2 calculated by using a typical within-study sampling variance from the Q statistic, the harmonic mean and the arithmatic mean of the

R2

Logical. If TRUE and there are predictors, R2 is calculated (Raudenbush, 2009).

model.name

A string for the model name in mxModel.

suppressWarnings

Logical. If TRUE, warnings are suppressed. Argument to be passed to mxRun.

silent

Logical. Argument to be passed to mxRun

run

Logical. If FALSE, only return the mx model without running the analysis.

...

Futher arguments to be passed to mxRun

Value

• An object of class meta with a list of
• call
  Object returned by match.call
• data
  A data matrix of y, v and x
• no.y
  No. of effect sizes
• no.x
  No. of predictors
• miss.x
  A vector indicating whether the predictors are missing. Studies will be removed before the analysis if they are TRUE
• I2
  Types of I2 calculated
• R2
  Logical
• mx.fit
  A fitted object returned from mxRun
• mx0.fit
  A fitted object without any predictor returned from mxRun

References

Cheung, M. W.-L. (2008). A model for integrating fixed-, random-, and mixed-effects meta-analyses into structural equation modeling. Psychological Methods, 13, 182-202.

Cheung, M. W.-L. (2009). Constructing approximate confidence intervals for parameters with structural equation models. Structural Equation Modeling, 16, 267-294.

Cheung, M. W.-L. (2013). Multivariate meta-analysis as structural equation models. Structural Equation Modeling, 20, 429-454. Hardy, R. J., & Thompson, S. G. (1996). A likelihood approach to meta-analysis with random effects. Statistics in Medicine, 15, 619-629. Neale, M. C., & Miller, M. B. (1997). The use of likelihood-based confidence intervals in genetic models. Behavior Genetics, 27, 113-120.

Raudenbush, S. W. (2009). Analyzing effect sizes: random effects models. In H. M. Cooper, L. V. Hedges, & J. C. Valentine (Eds.), The handbook of research synthesis and meta-analysis (2nd ed., pp. 295-315). New York: Russell Sage Foundation.

Xiong, C., Miller, J. P., & Morris, J. C. (2010). Measuring study-specific heterogeneity in meta-analysis: application to an antecedent biomarker study of alzheimer's disease. Statistics in Biopharmaceutical Research, 2(3), 300-309. doi:10.1198/sbr.2009.0067

See Also

reml, Hox02, Berkey98, wvs94a