metagen {meta} | R Documentation |
Fixed and random effects meta-analysis based on estimates (e.g. log hazard ratios) and their standard errors; inverse variance weighting is used for pooling.
metagen(TE, seTE, studlab, data=NULL, subset=NULL, sm="", level = 0.95, level.comb = level, comb.fixed=TRUE, comb.random=TRUE, title="", complab="", outclab="", label.e="Experimental", label.c="Control", byvar, bylab, print.byvar=TRUE)
TE |
Estimate of treatment effect. |
seTE |
Standard error of treatment estimate. |
studlab |
An optional vector with study labels. |
data |
An optional data frame containing the study information. |
subset |
An optional vector specifying a subset of studies to be used. |
sm |
A character string indicating underlying summary measure,
e.g., "RD" , "RR" , "OR" , "AS" ,
"MD" , "SMD" . |
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. |
title |
Title of meta-analysis / systematic review. |
complab |
Comparison label. |
outclab |
Outcome label. |
label.e |
Label for experimental group. |
label.c |
Label for control group. |
byvar |
An optional vector containing grouping information (must
be of same length as TE ). |
bylab |
A character string with a label for the grouping variable. |
print.byvar |
A logical indicating whether the name of the grouping variable should be printed in front of the group labels. |
Generic method for meta-analysis, only treatment estimates and their standard error are needed. The method is useful, e.g., for pooling of survival data (using log hazard ratio and standard errors as input). The inverse variance method is used for pooling. Random effects estimate is based on the DerSimonian-Laird method.
Internally, both fixed effect and random effects models are
calculated regardless of values choosen for arguments
comb.fixed
and comb.random
. Accordingly, the estimate
for the random effects model can be extracted from component
TE.random
of an object of class "meta"
even if
comb.random=FALSE
. However, all functions in R package
meta
will adequately consider the values for comb.fixed
and comb.random
. E.g. function print.meta
will
not print results for the random effects model if
comb.random=FALSE
.
An object of class c("metagen", "meta")
with corresponding
print
, summary
, plot
function. The object is a
list containing the following components:
TE, seTE, studlab, |
|
sm, level, level.comb, |
|
comb.fixed, comb.random, |
|
byvar, bylab, print.byvar |
As defined above. |
w.fixed, w.random |
Weight of individual studies (in fixed and random effects model). |
TE.fixed, seTE.fixed |
Estimated overall treatment effect and standard error (fixed effect model). |
TE.random, seTE.random |
Estimated overall treatment effect and standard error (random effects model). |
k |
Number of studies combined in meta-analysis. |
Q |
Heterogeneity statistic. |
tau |
Square-root of between-study variance (moment estimator of DerSimonian-Laird). |
method |
Pooling method: "Inverse" . |
call |
Function call. |
Guido Schwarzer sc@imbi.uni-freiburg.de
Cooper H & Hedges LV (1994), The Handbook of Research Synthesis. Newbury Park, CA: Russell Sage Foundation.
data(Fleiss93) meta1 <- metabin(event.e, n.e, event.c, n.c, data=Fleiss93, sm="RR", meth="I") meta1 ## ## Identical results by using the following commands: ## meta1 metagen(meta1$TE, meta1$seTE, sm="RR") ## ## Meta-analysis of survival data: ## logHR <- log(c(0.95, 1.5)) selogHR <- c(0.25, 0.35) metagen(logHR, selogHR, sm="HR")