meta-sm {meta}

Description of summary measures available in R package meta

Description

Description of summary measures available in R package meta

Details

The following summary measures (argument sm) are recognized in R package meta.

Meta-analysis of binary outcome data (metabin)

Argument	Summary measure
sm = "OR"	Odds ratio (Fleiss, 1993)
sm = "RR"	Risk ratio (Fleiss, 1993)
sm = "RD"	Risk difference (Fleiss, 1993)
sm = "ASD"	Arcsine difference (Rücker et al., 2009)
sm = "DOR"	Diagnostic odds ratio (Moses et al., 1993)
sm = "VE"	Vaccine efficacy or vaccine effectiveness

Note, mathematically, odds ratios and diagnostic odds ratios are identical, however, the labels in printouts and figures differ. Furthermore, log risk ratio (logRR) and log vaccine ratio (logVR) are mathematical identical, however, back-transformed results differ as vaccine efficacy or effectiveness is defined as VE = 100 * (1 - RR).

A continuity correction is used for some summary measures in the case of a zero cell count (see metabin).

List elements TE, TE.common, TE.random, etc., contain transformed values, e.g., log odds ratios, log risk ratios or log vaccine ratios. In printouts and plots transformed values are back transformed if argument backtransf = TRUE (default), with exception of the arcsine difference where no back-transformation exists. Auxiliary function logVR2VE is used to back-transform log vaccine ratios to vaccine efficacy or effectiveness while exp is used to back-transform log odds or risk ratios.

Meta-analysis of continuous outcome data (metacont)

Argument	Summary measure
sm = "MD"	Mean difference
sm = "SMD"	Standardised mean difference
sm = "ROM"	Ratio of means

Three variants to calculate the standardised mean difference are available (see metacont).

For the ratio of means, list elements TE, TE.common, TE.random, etc., contain the log transformed ratio of means. In printouts and plots these values are back transformed using exp if argument backtransf = TRUE (default).

Meta-analysis of correlations (metacor)

Argument	Summary measure
sm = "ZCOR"	Fisher's z transformed correlation
sm = "COR"	Untransformed correlations

For Fisher's z transformed correlations, list elements TE, TE.common, TE.random, etc., contain the transformed correlations. In printouts and plots these values are back transformed using auxiliary function z2cor if argument backtransf = TRUE (default).

Meta-analysis of incidence rates (metainc)

Argument	Summary measure
sm = "IRR"	Incidence rate ratio
sm = "IRD"	Incidence rate difference
sm = "IRSD"	Square root transformed incidence rate difference
sm = "VE"	Vaccine efficacy or vaccine effectiveness

Note, log incidence rate ratio (logIRR) and log vaccine ratio (logVR) are mathematical identical, however, back-transformed results differ as vaccine efficacy or effectiveness is defined as VE = 100 * (1 - IRR).

List elements TE, TE.common, TE.random, etc., contain the transformed incidence rates. In printouts and plots these values are back transformed if argument backtransf = TRUE (default). For back-transformation, exp is used for the incidence rate ratio, power of 2 is used for square root transformed rates and logVR2VE is used for vaccine efficacy / effectiveness.

Meta-analysis of single means (metamean)

Argument	Summary measure
sm = "MRAW"	Raw, i.e. untransformed, means
sm = "MLN"	Log transformed means

Calculations are conducted on the log scale if sm = "MLN". Accordingly, list elements TE, TE.common, and TE.random contain the logarithm of means. In printouts and plots these values are back transformed using exp if argument backtransf = TRUE.

Meta-analysis of single proportions (metaprop)

The following transformations of proportions are implemented to calculate an overall proportion:

Argument	Summary measure
sm = "PLOGIT"	Logit transformation
sm = "PAS"	Arcsine transformation
sm = "PFT"	Freeman-Tukey Double arcsine transformation
sm = "PLN"	Log transformation
sm = "PRAW"	No transformation

List elements TE, TE.common, TE.random, etc., contain the transformed proportions. In printouts and plots these values are back transformed if argument backtransf = TRUE (default). For back-transformation, logit2p is used for logit transformed proportions, asin2p is used for (Freeman-Tukey) arcsine transformed proportions and exp is used for log transformed proportions.

Meta-analysis of single rates (metarate)

The following transformations of incidence rates are implemented to calculate an overall rate:

Argument	Summary measure
sm = "IRLN"	Log transformation
sm = "IRS"	Square root transformation
sm = "IRFT"	Freeman-Tukey Double arcsine transformation
sm = "IR"	No transformation

List elements TE, TE.common, TE.random, etc., contain the transformed incidence rates. In printouts and plots these values are back transformed if argument backtransf = TRUE (default). For back-transformation, exp is used for log transformed rates, power of 2 is used for square root transformed rates and asin2ir is used for Freeman-Tukey arcsine transformed rates.

Generic inverse variance method (metagen)

The following summary measures are recognised in addition to the above mentioned summary measures:

Argument	Summary measure
sm = "HR"	Hazard ratio
sm = "VE"	Vaccine efficacy or vaccine effectiveness

List elements TE, TE.common, TE.random, etc., contain transformed values, i.e., log hazard ratios and log vaccine ratios. In printouts and plots these values are back transformed if argument backtransf = TRUE (default); see also meta-transf.

Author(s)

Guido Schwarzer [e-mail]

References

Borenstein M, Hedges LV, Higgins JP, Rothstein HR (2010): A basic introduction to fixed-effect and random-effects models for meta-analysis. Research Synthesis Methods, 1, 97–111

Fleiss JL (1993): The statistical basis of meta-analysis. Statistical Methods in Medical Research, 2, 121–45

Moses LE, Shapiro D, Littenberg B (1993): Combining Independent Studies of a Diagnostic Test into a Summary Roc Curve: Data-Analytic Approaches and Some Additional Considerations. Statistics in Medicine, 12, 1293–1316

Rücker G, Schwarzer G, Carpenter J, Olkin I (2009): Why add anything to nothing? The arcsine difference as a measure of treatment effect in meta-analysis with zero cells. Statistics in Medicine, 28, 721–38

Stijnen T, Hamza TH, Ozdemir P (2010): Random effects meta-analysis of event outcome in the framework of the generalized linear mixed model with applications in sparse data. Statistics in Medicine, 29, 3046–67

See Also

meta-package, meta-transf, meta-object, print.meta, summary.meta, forest.meta