rmeta (version 3.0)

meta.summaries: Meta-analysis based on effect estimates

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 meta.summaries
summary(object,conf.level=NULL,…)
# S3 method for 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.

Value

An object of class meta.summaries, which has print,plot,summary and funnelplot 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.

See Also

meta.DSL, meta.MH, funnelplot, metaplot

Examples

# NOT RUN {
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)
# }