metagen: Generic inverse variance meta-analysis

Description

Fixed and random effects meta-analysis based on estimates and their standard errors; inverse variance weighting is used for pooling.

Usage

metagen(TE, seTE, studlab, data=NULL, subset=NULL, sm="")

Arguments

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.

A character string indicating underlying summary measure, e.g., "RD", "RR", "OR", "WMD", "SMD".

Value

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,As defined above.
sm
w.fixed, w.randomWeight of individual studies (in fixed and random effects model).
TE.fixed, seTE.fixedEstimated overall treatment effect and standard error (fixed effect model).
TE.random, seTE.randomEstimated overall treatment effect and standard error (random effects model).
kNumber of studies combined in meta-analysis.
QHeterogeneity statistic.
tauSquare-root of between-study variance (moment estimator of DerSimonian-Laird).
methodPooling method: "Inverse".
callFunction call.

Details

Generic method for meta-analysis, only treatment estimates and their standard error are needed. The method is useful, e.g., for pooling of log hazard ratios. The inverse variance method is used for pooling. Random effects estimate is based on the DerSimonian-Laird method.

References

Cooper H & Hedges LV (1994), The Handbook of Research Synthesis. Newbury Park, CA: Russell Sage Foundation.

Examples

Run this code

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")

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