metainf | R Documentation |

Performs an influence analysis. Pooled estimates are calculated omitting one study at a time.

metainf(x, pooled, sortvar, no = 1)

`x` |
An object of class |

`pooled` |
A character string indicating whether a common effect
or random effects model is used for pooling. Either missing (see
Details), |

`sortvar` |
An optional vector used to sort the individual
studies (must be of same length as |

`no` |
A numeric specifying which meta-analysis results to consider. |

Performs a influence analysis; pooled estimates are calculated
omitting one study at a time. Studies are sorted according to
`sortvar`

.

Information from object `x`

is utilised if argument
`pooled`

is missing. A common effect model is assumed
(`pooled="common"`

) if argument `x$common`

is
`TRUE`

; a random effects model is assumed
(`pooled="random"`

) if argument `x$random`

is
`TRUE`

and `x$common`

is `FALSE`

.

An object of class `"meta"`

and `"metainf"`

with
corresponding generic functions (see `meta-object`

).

The following list elements have a different meaning:

`TE, seTE` |
Estimated treatment effect and standard error of pooled estimate in influence analysis. |

`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 omission of studies. |

`w` |
Sum of weights from common effect or random effects model. |

`TE.common, seTE.common` |
Value is |

`TE.random, seTE.random` |
Value is |

`Q` |
Value is |

Guido Schwarzer sc@imbi.uni-freiburg.de

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

`metabin`

, `metacont`

,
`print.meta`

data(Fleiss1993bin) m1 <- metabin(d.asp, n.asp, d.plac, n.plac, data = Fleiss1993bin, studlab = study, sm = "RR", method = "I") m1 metainf(m1) metainf(m1, pooled = "random") forest(metainf(m1)) forest(metainf(m1), layout = "revman5") forest(metainf(m1, pooled = "random")) metainf(m1, sortvar = study) metainf(m1, sortvar = 7:1) m2 <- update(m1, title = "Fleiss1993bin meta-analysis", backtransf = FALSE) metainf(m2) data(Fleiss1993cont) m3 <- metacont(n.psyc, mean.psyc, sd.psyc, n.cont, mean.cont, sd.cont, data = Fleiss1993cont, sm = "SMD") metainf(m3)

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