metasumm: Meta-analysis summary statistics

In chjackson/MetaAnalyser: An Interactive Visualisation of Meta-Analysis as a Physical Weighing Machine

Description

Compute meta-analysis weights and corresponding pooled estimates given a set of estimates and standard errors. Weights are simply defined by the inverse variance, where the variance is the sum of the study-specific and random effects variance.

Usage

metasumm(dat, resd, egger = FALSE)

Arguments

dat	Meta-analysis data. This should be a data frame with three columns, called "name", "est" and "se" giving the study name, study-specific parameter estimates and corresponding standard errors respectively. Numeric or character study names are permitted. If the data frame has more than three columns, the first three are used. If the first three columns are called "name", "est" and "se" in some order, they are re-ordered appropriately, otherwise they are re-named.
resd	Random effects standard deviation. Set resd=0 for a fixed effects meta-analysis. If resd is omitted, a random effects meta-analysis is performed using the typical DerSimonian and Laird method to obtain the standard deviation (resd_dsl).
egger	Set to TRUE to perform Egger correction.

Value

A list with the following components:

est	Original study-specific estimates (if egger=FALSE) or Egger-corrected version of these (if egger=TRUE).
pool	Pooled estimate
poolse	Pooled standard error
poolci	Pooled 95% confidence interval
pwtfe	Weights for fixed effects model, normalised to sum to 1
pwtre	Weights for desired random effects standard deviation, normalised to sum to 1

chjackson/MetaAnalyser documentation built on May 13, 2019, 5:28 p.m.