mixmetaCovStruct: (Co)variance Structures for mixmeta Models

Description Options Details Note Author(s) References See Also Examples

Description

Alternative options for the (co)variance structure of the random effects random effects in meta-analytical models, usually defined through the argument bscov of the function mixmeta.

Options

Assuming a meta-analysis or meta-regression based on k outcomes, for each grouping level with q random-effects predictors the matrix can be specified in various forms listed below. For multivariate models with multiple predictors, the order implies a sequence of q parameters for each k outcomes. These are the options:

Details

Structures other than unstr are only available for models estimated through (restricted) maximum likelihood.

The unrestricted parameters defining the random-effects (co)variance matrix (or matrices for multilvel models) are estimated in the iterative optimization algorithm (see mixmeta.ml). Although rarely needed and not recommeded, the user can provided a starting value of the (co)variance matrix, from which the parameters are derived (see mixmeta.control).

Note

The choice of structures can affect the performance of the optimization procedure, determining forms of likelihood surfaces which induce convergence to local maxima. In particular, structures such as multiple of identity or proportional to a fixed matrix are based on strong assumptions and should be used with caution.

Author(s)

Antonio Gasparrini <antonio.gasparrini@lshtm.ac.uk> and Francesco Sera <francesco.sera@lshtm.ac.uk>

References

Sera F, Armstrong B, Blangiardo M, Gasparrini A (2019). An extended mixed-effects framework for meta-analysis.Statistics in Medicine. 2019;38(29):5429-5444. [Freely available here].

Pinheiro JC and Bates DM (2000). Mixed-Effects Models in S and S-PLUS. New York, Springer Verlag.

See Also

See mixmeta. See lm or glm for standard regression functions. See mixmeta-package for an overview of this modelling framework.

Examples

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# UNSTRUCTURED AND STRUCTURED BETWEEN-STUDY (CO)VARIANCE
y <- as.matrix(smoking[11:13])
S <- as.matrix(smoking[14:19])
mod1 <- mixmeta(y, S)
summary(mod1)
mod1$Psi

# DIAGONAL
mod2 <- mixmeta(y, S, bscov="diag")
summary(mod2)
mod2$Psi

# HETEROGENEOUS COMPOUND SYMMETRY
mod3 <- mixmeta(y, S, bscov="hcs")
summary(mod3)
mod3$Psi

# PROPORTIONAL
mod4 <- mixmeta(y, S, bscov="prop", control=list(Psifix=diag(3)+1))
summary(mod4)
mod4$Psi

# CORRELATION
Psicor <- matrix(0.2, 3, 3) ; diag(Psicor) <- 1
mod5 <- mixmeta(y, S, bscov="cor", control=list(Psifix=Psicor))
summary(mod5)
mod5$Psi

mixmeta documentation built on Oct. 16, 2021, 5:09 p.m.