LS {remaCor}R Documentation

Fixed effect meta-analysis for correlated test statistics

Description

Fixed effect meta-analysis for correlated test statistics using the Lin-Sullivan method.

Usage

LS(beta, stders, cor = diag(1, length(beta)))

Arguments

beta

regression coefficients from each analysis

stders

standard errors corresponding to betas

cor

correlation matrix between of test statistics. Default considers uncorrelated test statistics

Details

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.

Value

Return values:

References

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.

Examples

# 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)


[Package remaCor version 0.0.11 Index]