meta (version 4.6-0)

metacr: Meta-analysis of outcome data from Cochrane review

Description

Wrapper function to perform meta-analysis for a single outcome of a Cochrane Intervention review.

Usage

metacr(x, comp.no=1, outcome.no=1, method, sm, level=.settings$level, level.comb=.settings$level.comb, comb.fixed, comb.random, hakn=FALSE, method.tau="DL", tau.common=FALSE, prediction=.settings$prediction, level.predict=.settings$level.predict, swap.events, logscale, backtransf=.settings$backtransf, title, complab, outclab, keepdata=.settings$keepdata, warn=FALSE)

Arguments

x

An object of class rm5 created by R function read.rm5.

comp.no

Comparison number.

outcome.no

Outcome number.

method

A character string indicating which method is to be used for pooling of studies. One of "Inverse", "MH", or "Peto", can be abbreviated.

sm

A character string indicating which summary measure ("RR", "OR", "RD", "ASD", "HR", "MD", or "SMD", or "ROM") is to be used for pooling of studies.

level

The level used to calculate confidence intervals for individual studies.

level.comb

The level used to calculate confidence intervals for pooled estimates.

comb.fixed

A logical indicating whether a fixed effect meta-analysis should be conducted.

comb.random

A logical indicating whether a random effects meta-analysis should be conducted.

hakn

A logical indicating whether the method by Hartung and Knapp should be used to adjust test statistics and confidence intervals.

method.tau

A character string indicating which method is used to estimate the between-study variance $\tau^2$. Either "DL", "PM", "REML", "ML", "HS", "SJ", "HE", or "EB", can be abbreviated.

tau.common

A logical indicating whether tau-squared should be the same across subgroups.

prediction

A logical indicating whether a prediction interval should be printed.

level.predict

The level used to calculate prediction interval for a new study.

swap.events

A logical indicating whether events and non-events should be interchanged.

logscale

A logical indicating whether effect estimates are entered on log-scale.

backtransf

A logical indicating whether results should be back transformed in printouts and plots. If backtransf=TRUE (default), results for sm="OR" are printed as odds ratios rather than log odds ratios and results for sm="ZCOR" are printed as correlations rather than Fisher's z transformed correlations, for example.

title

Title of meta-analysis / systematic review.

complab

Comparison label.

outclab

Outcome label.

keepdata

A logical indicating whether original data (set) should be kept in meta object.

warn

A logical indicating whether warnings should be printed (e.g., if incr is added to studies with zero cell frequencies).

Value

An object of class "meta" and "metabin", "metacont", or "metagen" depending on outcome type utilised in Cochrane Intervention review for selected outcome.

Details

Cochrane Intervention reviews are based on the comparison of two interventions. Each Cochrane Intervention review can have a variable number of comparisons. For each comparison, a variable number of outcomes can be define. For each outcome, a seperate meta-analysis is conducted. Review Manager 5 (RevMan 5) is the current software used for preparing and maintaining Cochrane Reviews (http://tech.cochrane.org/revman/). This wrapper function can be used to perform meta-analysis for a single outcome of a Cochrane Intervention review. Internally, R functions metabin, metacont, and metagen are called - depending on the definition of the outcome in RevMan 5.

References

Review Manager (RevMan) [Computer program]. Version 5.3. Copenhagen: The Nordic Cochrane Centre, The Cochrane Collaboration, 2014.

See Also

metabin, metacont, metagen, read.rm5

Examples

# Locate export data file "Fleiss93_CR.csv"
# in sub-directory of package "meta"
#
filename <- system.file("data/Fleiss93_CR.csv.gz", package = "meta")
#
Fleiss93_CR <- read.rm5(filename)

# Same result as R command example(Fleiss93):
#
metacr(Fleiss93_CR)

# Same result as R command example(Fleiss93cont):
#
metacr(Fleiss93_CR, 1, 2)

forest(metacr(Fleiss93_CR, 1, 2))

# Change summary measure to RR
#
m1 <- metacr(Fleiss93_CR)
update(m1, sm="RR")