meta (version 6.5-0)

metaadd: Add pooled results from external analysis to meta-analysis

Description

Add pooled results from external analysis to an existing meta-analysis object. This is useful, for example, to add results from a Bayesian meta-analysis which is not implemented in R package meta.

Usage

metaadd(
  x,
  type,
  TE,
  lower,
  upper,
  statistic = NA,
  pval = NA,
  text,
  data = NULL
)

Value

An object of class "meta" with corresponding generic functions (see meta-object).

Arguments

x

Meta-analysis object.

type

A character string or vector indicating whether added results are from common effect, random effects model or prediction interval. Either "common", "random" or "prediction", can be abbreviated.

TE

Pooled estimate(s).

lower

Lower limit(s) of confidence or prediction interval.

upper

Upper limit(s) of confidence or prediction interval.

statistic

Test statistic(s).

pval

P-value(s).

text

A character string or vector used in printouts and forest plot to label the added results.

data

An optional data frame containing the new results.

Author

Guido Schwarzer

Details

In R package meta, objects of class "meta" contain results of both common effect and random effects meta-analyses. This function enables the user to add the pooled results of an additional analysis to an existing meta-analysis object. This is useful, for example, to add the result of a Bayesian meta-analysis.

Arguments TE, lower and upper have to be provided if type = "common" or type = "random". For type = "prediction", only arguments lower and upper have to be provided and other arguments are ignored.

Note, R function metamerge can be used to add meta-analysis results of another meta-analysis object (see meta-object).

See Also

metagen, metamerge

Examples

data(Fleiss1993bin)

# Use REML estimator of tau2 (default)
m1 <- metabin(d.asp, n.asp, d.plac, n.plac, data = Fleiss1993bin,
  studlab = paste(study, year), sm = "OR",
  text.random = "Random effects model (REML)", overall.hetstat = FALSE)

# Use DerSimonian-Laird estimator of tau2
m2 <- update(m1, method.tau = "DL")

# Add random effects results of second meta-analysis
m12 <- metaadd(m1, type = "random", data = m2,
  TE = TE.random,
  lower = lower.random, upper = upper.random,
  statistic = statistic.random, pval = pval.random,
  text = "Random effects model (DL)")
m12

forest(m12)