mmm: Multivariate meta-analysis of correlated effects

In CIAAWconsensus: Isotope Ratio Meta-Analysis

Description

This function provides meta-analysis of multivariate correlated data using the marginal method of moments with working independence assumption as described by Chen et al (2016). As such, the meta-analysis does not require correlations between the outcomes within each dataset.

Usage

mmm(y, uy, knha = TRUE, verbose = TRUE)

Arguments

y	A matrix of results from each of the n laboratories (rows) where each study reports m isotope ratios (columns)
uy	A matrix with uncertainties of the results given in y
knha	(Logical) Allows for the adjustment of consensus uncertainties using the Birge ratio (Knapp-Hartung adjustment)
verbose	(Logical) Requests annotated summary output of the results

Details

The marginal method of moments delivers the inference for correlated effect sizes using multiple univariate meta-analyses.

Value

studies	The number of independent studies
beta	The consensus estimates for all outcomes
beta.u	Standard uncertainties of the consensus estimates
beta.U95	Expanded uncertainties of the consensus estimates corresponding to 95% confidence
beta.cov	Covariance matrix of the consensus estimates
beta.cor	Correlation matrix of the consensus estimates
H	Birge ratios (Knapp-Hartung adjustment) which were applied to adjust the standard uncertainties of each consensus outcome
I2	Relative total variability due to heterogeneity (in percent) for each outcome

Author(s)

Juris Meija <[e-mail]> and Antonio Possolo

References

Y. Chen, Y. Cai, C. Hong, and D. Jackson (2016) Inference for correlated effect sizes using multiple univariate meta-analyses. Statistics in Medicine, 35, 1405-1422

J. Meija and A. Possolo (2017) Data reduction framework for standard atomic weights and isotopic compositions of the elements. Metrologia, 54, 229-238

Examples

## Consensus isotope amount ratios for platinum
df=normalize.ratios(platinum.data, "platinum", "195Pt")
mmm(df$R, df$u.R)

Example output

Multivariate Marginal Method of Moments with working independence assumption 
Chen et al (2016) DOI: 10.1002/sim.6789 
 
Number of studies:  4 
Isotope ratios 
 190Pt/195Pt 192Pt/195Pt 194Pt/195Pt 196Pt/195Pt 198Pt/195Pt 
 
Consensus values 
 0.0003826 0.0232464 0.9728039 0.7471211 0.2145943 
 
Standard uncertainty 
 0.0000106 0.0001444 0.0015868 0.0010886 0.0016349 
 
Expanded uncertainty, 95 % 
 0.0000308 0.0004199 0.0046150 0.0031661 0.0047546 
 
Birge ratio (Knapp-Hartung adjustment) for each outcome 
 1.18 1.00 1.00 1.00 1.00 
 
Relative total variability due to heterogeneity (%) for each outcome 
 95.6 99.6 99.3 90.4 98.4 
 

CIAAWconsensus documentation built on May 2, 2019, 3:33 p.m.