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metacum

Cumulative meta-analysis

Description

Performs a cumulative meta-analysis.

Usage

metacum(x, pooled, sortvar)

Arguments

`x`	An object of class `meta`.
`pooled`	A character string indicating whether a fixed effect or random effects model is used for pooling. Either missing (see Details), `"fixed"`, or `"random"`, can be abbreviated.
`sortvar`	An optional vector used to sort the individual studies (must be of same length as `x$TE`).

Details

A cumulative meta-analysis is performed. Studies are included sequentially as defined by sortvar.

Information from object x is utilised if argument pooled is missing. A fixed effect model is assumed (pooled = "fixed") if argument x$comb.fixed is TRUE; a random effects model is assumed (pooled = "random") if argument x$comb.random is TRUE and x$comb.fixed is FALSE.

Value

An object of class c("metacum", "meta") with corresponding print, and forest functions. The object is a list containing the following components:

`TE, seTE`	Estimated treatment effect and standard error of pooled estimate in cumulative meta-analyses.
`lower, upper`	Lower and upper confidence interval limits.
`statistic`	Statistic for test of overall effect.
`pval`	P-value for test of overall effect.
`studlab`	Study label describing addition of studies.
`w`	Sum of weights from fixed effect or random effects model.
`I2`	Heterogeneity statistic I^2.
`Rb`	Heterogeneity statistic R_b.
`tau`	Square-root of between-study variance.
`df.hakn`	Degrees of freedom for test of treatment effect for Hartung-Knapp method (only if `hakn = TRUE`).
`sm`	Summary measure.
`method`	Method used for pooling.
`k`	Number of studies combined in meta-analysis.
`pooled`	As defined above.
`comb.fixed`	A logical indicating whether analysis is based on fixed effect model.
`comb.random`	A logical indicating whether analysis is based on random effects model.
`TE.fixed, seTE.fixed`	Value is `NA`.
`TE.random, seTE.random`	Value is `NA`.
`Q`	Value is `NA`.
`level.comb`	The level used to calculate confidence intervals for pooled estimates.
`hakn`	A logical indicating whether the method by Hartung and Knapp is used to adjust test statistics and confidence intervals.
`adhoc.hakn`	A character string indicating whether ad hoc variance correction should be used for Hartung-Knapp method.
`method.tau`	A character string indicating which method is used to estimate the between-study variance τ^2.
`tau.preset`	Prespecified value for the square root of the between-study variance τ^2.
`TE.tau`	Overall treatment effect used to estimate the between-study variance τ^2.
`n.harmonic.mean`	Harmonic mean of number of observations (for back transformation of Freeman-Tukey Double arcsine transformation).
`version`	Version of R package meta used to create object.

Author(s)

Guido Schwarzer sc@imbi.uni-freiburg.de

References

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

Examples

data(Fleiss1993bin)
m1 <- metabin(d.asp, n.asp, d.plac, n.plac,
              data = Fleiss1993bin, studlab = study,
              sm = "RR", method = "I")
m1
metacum(m1)
metacum(m1, pooled = "random")

forest(metacum(m1))
forest(metacum(m1, pooled = "random"))

metacum(m1, sortvar = study)
metacum(m1, sortvar = 7:1)

m2 <- update(m1, title = "Fleiss1993bin meta-analysis",
             backtransf = FALSE)
metacum(m2)

data(Fleiss1993cont)
m3 <- metacont(n.psyc, mean.psyc, sd.psyc, n.cont, mean.cont, sd.cont,
               data = Fleiss1993cont, sm = "SMD")
metacum(m3)