Multivariate meta-analysis based on generalized least squares
This function accepts a data matrix of parameter estimates and their variance-covariance matrix from individual studies and obtain a generalized least squares (GLS) estimate and heterogeneity statistic.
For instance, this would be appropriate for combining linear correlation coefficients of single nucleotide polymorphisms (SNPs) for a given region.
mvmeta(b,V)
b |
the parameter estimates |
V |
the triangular variance-covariance matrix |
The returned value is a list containing:
d |
the compact parameter estimates |
Psi |
the compact covariance-covariance matrix |
X |
the design matrix |
beta |
the pooled parameter estimates |
cov.beta |
the pooled variance-covariance matrix |
X2 |
the Chi-squared statistic for heterogeneity |
df |
the degrees(s) of freedom |
p |
the p value |
Hartung J, Knapp G, Sinha BK. Statistical Meta-analysis with Applications, Wiley 2008.
Jing Hua Zhao
## Not run: # example 11.3 from Hartung et al. # b <- matrix(c( 0.808, 1.308, 1.379, NA, NA, NA, 1.266, 1.828, 1.962, NA, NA, 1.835, NA, 2.568, NA, NA, 1.272, NA, NA, 2.038, 1.171, 2.024, 2.423, 3.159, NA, 0.681, NA, NA, NA, NA),ncol=5, byrow=TRUE) psi1 <- psi2 <- psi3 <- psi4 <- psi5 <- psi6 <- matrix(0,5,5) psi1[1,1] <- 0.0985 psi1[1,2] <- 0.0611 psi1[1,3] <- 0.0623 psi1[2,2] <- 0.1142 psi1[2,3] <- 0.0761 psi1[3,3] <- 0.1215 psi2[2,2] <- 0.0713 psi2[2,3] <- 0.0539 psi2[2,4] <- 0.0561 psi2[3,3] <- 0.0938 psi2[3,4] <- 0.0698 psi2[4,4] <- 0.0981 psi3[2,2] <- 0.1228 psi3[2,4] <- 0.1119 psi3[4,4] <- 0.1790 psi4[2,2] <- 0.0562 psi4[2,5] <- 0.0459 psi4[5,5] <- 0.0815 psi5[1,1] <- 0.0895 psi5[1,2] <- 0.0729 psi5[1,3] <- 0.0806 psi5[1,4] <- 0.0950 psi5[2,2] <- 0.1350 psi5[2,3] <- 0.1151 psi5[2,4] <- 0.1394 psi5[3,3] <- 0.1669 psi5[3,4] <- 0.1609 psi5[4,4] <- 0.2381 psi6[1,1] <- 0.0223 V <- rbind(psi1[upper.tri(psi1,diag=TRUE)],psi2[upper.tri(psi2,diag=TRUE)], psi3[upper.tri(psi3,diag=TRUE)],psi4[upper.tri(psi4,diag=TRUE)], psi5[upper.tri(psi5,diag=TRUE)],psi6[upper.tri(psi6,diag=TRUE)]) mvmeta(b,V) ## End(Not run)
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