metahdep.REMA {metahdep} | R Documentation |
Performs a random effects linear model meta-analysis, allowing for hierarchical dependence. It returns a list containing the results.
metahdep.REMA(theta, V, X, M = NULL, dep.groups = NULL, meta.name = "meta-analysis", delta.split = FALSE, center.X = FALSE)
theta |
A vector of effect size estimates from multiple studies. |
V |
The variance/covariance matrix for |
X |
A matrix of covariates for |
M |
(optional) Used when |
dep.groups |
(optional) Used when |
meta.name |
(optional) A name field for bookkeeping. This can be any character string. |
delta.split |
(optional) A logical value specifying whether or not to account for hierarchical dependence (i.e., perform delta-splitting). If |
center.X |
(optional) A logical value specifying whether or not to center the columns of |
Takes a vector of effect size estimates, a variance/covariance matrix, and a covariate matrix, and fits a random effects linear model meta-analysis, allowing for hierarchical dependence.
If delta.split=TRUE
, then it performs delta-splitting to account for hierarchical dependence among studies.
When a meta-analysis is to be performed for gene expression data (on a per-gene basis), the metahdep()
function calls this metahdep.REMA()
function for each gene separately.
A list, with the following named components:
beta.hats |
A vector of model estimates for the covariates given by |
cov.matrix |
The variance/covariance matrix for the |
beta.hat.p.values |
The [two-sided] p-value(s) for the |
tau2.hat |
The estimated between-study hierarchical variance tau-square, using the method of moments approach of DerSimonian and Laird. |
varsigma.hat |
(Only estimated when |
Q |
The statistic used to test for model homogeneity / model mis-specification |
Q.p.value |
The p-value for |
name |
An optional name field |
John R. Stevens, Gabriel Nicholas
DerSimonian R. and Laird N. (1986), Meta-analysis in clinical trials, Controlled Clinical Trials, 7: 177-188.
Hedges L. V. and Olkin I (1985), Statistical methods for meta-analysis, San Diego, CA: Academic Press.
Stevens J.R. and Doerge R.W. (2005), Combining Affymetrix Microarray Results, BMC Bioinformatics, 6:57.
Stevens J.R. and Taylor A.M. (2009), Hierarchical Dependence in Meta-Analysis, Journal of Educational and Behavioral Statistics, 34(1):46-73.
See also the metahdep package vignette.
### ### Example 1: gene expression data ### - this uses one gene from the HGU.prep.list object # load data and extract components for meta-analysis (for one gene) data(HGU.prep.list) gene.data <- HGU.prep.list[[7]] theta <- gene.data@theta V <- gene.data@V X <- gene.data@X M <- gene.data@M dep.grps <- list(c(1:2),c(4:6)) gene.name <- gene.data@gene # fit a regular REMA (no hierarchical dependence) results <- metahdep.REMA(theta, V, X, meta.name=gene.name) results # fit hierarchical dependence model (with delta-splitting), # using two different methods for specifying the dependence structure results.dsplitM <- metahdep.REMA(theta, V, X, M, delta.split=TRUE, meta.name=gene.name, center.X=TRUE) results.dsplitM results.dsplitd <- metahdep.REMA(theta, V, X, dep.groups=dep.grps, delta.split=TRUE, meta.name=gene.name, center.X=TRUE) results.dsplitd ### ### Example 2: glossing data ### - this produces part of Table 6 in the Stevens and Taylor JEBS paper. data(gloss) dep.groups <- list(c(2,3,4,5),c(10,11,12)) REMA.ds <- metahdep.REMA(gloss.theta, gloss.V, gloss.X, center.X=TRUE, delta.split=TRUE, dep.groups=dep.groups) round(cbind(t(REMA.ds$beta.hats), sqrt(diag(REMA.ds$cov.matrix)), t(REMA.ds$beta.hat.p.values)),4)