Computes a summary estimate and confidence interval from a collection
of treatment effect estimates and standard errors. Allows fixed or
random effects, optional quality weights.
Usage
meta.summaries(d, se, method=c("fixed", "random"), weights=NULL,
logscale=FALSE, names=NULL, data=NULL,
conf.level=0.95, subset=NULL,na.action=na.fail)
## S3 method for class 'meta.summaries'
summary(object,conf.level=NULL,...)
## S3 method for class 'meta.summaries'
plot(x,summary=TRUE,summlabel="Summary",
conf.level=NULL,colors=meta.colors(),
xlab=NULL,logscale=NULL,...)
Arguments
d
Effect estimates
se
standard errors for d
method
Standard errors and default weights from fixed or
random-effects?
weights
Optional weights (eg quality weights)
logscale
Effect is on a log scale? (for plotting)
names
labels for the separate studies
data
optional data frame to find variables in
conf.level
level for confidence intervals
subset
Which studies to use
na.action
a function which indicates what should happen when
the data contain NAs. Defaults to na.fail.
x,object
a meta.summaries object
summary
Plot the summary odds ratio?
summlabel
Label for the summary odds ratio
colors
see meta.colors
xlab
label for the effect estimate axis.
...
further arguments to be passed to or from methods.
Details
The summary estimate is a weighted average. If weights are
specified they are used, otherwise the reciprocal of the estimated
variance is used.
The estimated variance is the square of se for a fixed
analysis. For a random analysis a heterogeneity variance is estimated
and added.
The variance of a weighted average is a weighted average of the
estimated variances using the squares of the weights. This is the
square of the summary standard error.
With the default weights these are the standard fixed and random
effects calculations.
Value
An object of class meta.summaries, which has
print,plot,summary and funnelplot
methods.
Author(s)
Thomas Lumley
See Also
meta.DSL,
meta.MH,
funnelplot,
metaplot
Examples
data(catheter)
b <- meta.DSL(n.trt, n.ctrl, col.trt, col.ctrl, data=catheter,
names=Name, subset=c(13,6,5,3,12,4,11,1,8,10,2))
d <- meta.summaries(b$logs, b$selogs, names=b$names,
method="random", logscale=TRUE)