meta.summaries: Meta-analysis based on effect estimates

In rmeta: Meta-Analysis

Description

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)

Example output

rmeta documentation built on May 2, 2019, 1:10 p.m.