meta (version 0.5)

metacont: Meta-analysis of continuous outcome data

Description

Calculation of fixed and random effects estimates for meta-analyses with continuous outcome data; inverse variance weighting is used for pooling.

Usage

metacont(n.e, mean.e, sd.e, n.c, mean.c, sd.c, studlab,
         data=NULL, subset=NULL, sm="WMD")

Arguments

n.e

Number of observations in experimental group.

mean.e

Estimated mean in experimental group.

sd.e

Standard deviation in experimental group.

n.c

Number of observations in control group.

mean.c

Estimated mean in control group.

sd.c

Standard deviation in control group.

studlab

An optional vector with study labels.

data

An optional data frame containing the study information, i.e., n.e, mean.e, sd.e, n.c, mean.c, and n.c.

subset

An optional vector specifying a subset of studies to be used.

sm

A character string indicating which summary measure ("WMD" or "SMD") is to be used for pooling of studies.

Value

• An object of class c("metacont", "meta") with corresponding print, summary, plot function. The object is a list containing the following components:
• n.e, mean.e, sd.e,
• n.c, mean.c, sd.c,
  As defined above.
• studlab, sm
• TE, seTE
  Estimated treatment effect and standard error of individual studies.
• w.fixed, w.random
  Weight of indiviudal 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.

Details

Calculation of fixed and random effects estimates for meta-analyses with continuous outcome data; inverse variance weighting is used for pooling. The DerSimonian-Laird estimate is used in the random effects model. For the summary measure "SMD", Hedges' adjusted g is utilised for pooling.

The function metagen is called internally to calculate individual and overall treatment estimates and standard errors.

References

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

See Also

metabin, metagen

Examples

data(Fleiss93cont)
meta1 <- metacont(n.e, mean.e, sd.e, n.c, mean.c, sd.c, data=Fleiss93cont, sm="SMD")
meta1

meta2 <- metacont(Fleiss93cont$n.e, Fleiss93cont$mean.e,
                  Fleiss93cont$sd.e,
                  Fleiss93cont$n.c, Fleiss93cont$mean.c,
                  Fleiss93cont$sd.c,
                  sm="SMD")
meta2