LS {remaCor} | R Documentation |
Fixed effect meta-analysis for correlated test statistics using the Lin-Sullivan method.
LS(beta, stders, cor = diag(1, length(beta)))
beta |
regression coefficients from each analysis |
stders |
standard errors corresponding to betas |
cor |
correlation matrix between of test statistics. Default considers uncorrelated test statistics |
Perform fixed effect meta-analysis for correlated test statistics using method of Lin and Sullivan (2009). By default, correlation is set to identity matrix to for independent test statistics.
This method requires the correlation matrix to be symmatric positive definite (SPD). If this condition is not satisfied, results will be NA. If the matrix is not SPD, there is likely an issue with how it was generated.
However, evaluating the correlation between observations that are not pairwise complete can give correlation matricies that are not SPD. In this case, consider running Matrix::nearPD( x, corr=TRUE)
to produce the nearest SPD matrix to the input.
Return values:
beta
: effect size
se
: effect size standard error
p
: p-value
Lin D, Sullivan PF (2009). “Meta-analysis of genome-wide association studies with overlapping subjects.” The American Journal of Human Genetics, 85(6), 862–872. https://doi.org/10.1016/j.ajhg.2009.11.001.
# Generate effects
library(mvtnorm)
library(clusterGeneration )
n = 4
Sigma = cov2cor(genPositiveDefMat(n)$Sigma)
beta = t(rmvnorm(1, rep(0, n), Sigma))
stders = rep(.1, n)
# Run fixed effects meta-analysis,
# assume identity correlation
LS( beta, stders)
# Run random effects meta-analysis,
# assume identity correlation
RE2C( beta, stders)
# Run fixed effects meta-analysis,
# account for correlation
LS( beta, stders, Sigma)
# Run random effects meta-analysis,
# account for correlation
RE2C( beta, stders, Sigma)