MetaHD {MetaHD}

A Multivariate Meta-Analysis Model for Metabolomics Data

Description

The MetaHD function performs a multivariate meta-analysis for combining summary estimates obtained from multiple metabolomic studies by using restricted maximum likelihood estimation. Assuming a meta-analysis is based on N outcomes/metabolites and K studies:

Usage

MetaHD(
  Y, Slist,
  Psi = NULL,
  method = c("reml", "fixed"),
  bscov = c("unstructured", "diag"),
  rigls.maxiter = 1,
  est.wscor = FALSE,
  shrinkCor = TRUE,
  impute.na = FALSE,
  impute.var = 10^4
)

Arguments

Y

: treatment effect sizes of the outcomes. This should be in the form of a K x N matrix

Slist

: K-dimensional list of N x N matrices representing within-study variances and covariances of the treatment effects. If within-study correlations are not available, input associated variances of treatment effects in the form of a K x N matrix and set est.wscor = TRUE.

Psi

: N x N matrix representing between-study variances and covariances of the treatment effects. (optional, if not specified this will be estimated internally by "MetaHD" using "estimateBSvar" and "estimateCorMat" functions in "MetaHD" package

method

: estimation method: "fixed" for fixed-effects models,"reml" for random-effects models fitted through restricted maximum likelihood

bscov

: a character vector defining the structure of the random-effects covariance matrix. Among available covariance structures, the user can select "unstructured" to obtain between-study covariance matrix with diagonal elements (variances) estimated using restricted maximul likelihood and off-diagonal elements (co-variances) reflecting the correlations estimated via shrinkage and "diag" (diagonal) for between-study variances as diagonal elements and zero co-variances

rigls.maxiter

: maximum number of iterations of the restricted iterative generalized least square algorithm. Default is set to 1

est.wscor

: a logical value indicating whether the within-study correlation matrix needs to be estimated or not. Default is FALSE

shrinkCor

: a logical value indicating whether a shrinkage estimator should be used to estimate within- or between-study correlation matrix. Default is TRUE

impute.na

: a logical value indicating whether missing values need to be imputed or not. Default is FALSE

impute.var

: multiplier for replacing the missing variances in Slist.(a large value, default is 10^4)

Value

A list of objects containing estimate : a N-dimensional vector of the combined estimates, std.err : a N-dimensional vector of the associated standard errors, pVal : a N-dimensional vector of the p-values, I2.stat : I2 statistic